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.

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.

At this point you don’t have to imagine this scenario anymore: A breaking news segment airs live, showing dramatic footage from a city center.

Within minutes, the clip is shared across social platforms, picked up by online outlets and debated by millions, including key on-air talent. Hours later, doubts emerge as to whether the footage was real or manipulated. Where did it come from? Suddenly, the broadcaster is thrust into a crisis, facing not only public skepticism but potential regulatory scrutiny. The talent is embarrassed; the viewers lose trust.

This isn’t a hypothetical. This is a growing daily reality in today’s media landscape. While we have dramatically increased the speed and reach of content distribution, we have also amplified risks of hurting our credibility.

According to an October Gallup poll, public confidence in mass media has dropped to near-record lows, with only 32% of Americans expressing “a great deal” or “quite a lot” of trust in news reporting. This erosion of trust is clearly a reputational issue. It directly impacts audience loyalty and ultimately, advertising revenue.

In this column, I explore the urgency of content provenance, define its core concepts in broadcast terms, review current standards and industry momentum and make the case for urgent adoption.

What Is Content Provenance in Broadcasting?
Content provenance in broadcasting refers to the systematic documentation and verification of a media asset’s origin or creation context, and subsequent edits or transformations throughout its lifecycle. While simple labeling might indicate where a clip was sourced or who produced it, provenance goes further. It creates a tamper-evident chain of custody, often using cryptographic methods like blockchain, that can be traced and audited at any point.

The technical foundation of provenance includes:

• Metadata Embedding: Attaching detailed, standardized metadata about content origin, production, and edits directly to media files.
• Cryptographic Signing: Using digital signatures to ensure that provenance information cannot be altered without detection.
• Verification Infrastructure: Systems that allow recipients—whether internal teams, syndication partners or even viewers—to validate provenance claims. This can be a complex analysis or as simple as a warning in a player app when provenance cannot be confirmed.
• Lifecycle Tracking: Recording every transformation, edit or redistribution, maintaining an immutable audit trail.

The Coalition for Content Provenance and Authenticity website (C2PA.org) provides more specific technical details. Provenance is especially important for video, where deepfakes and synthetic media can be nearly indistinguishable from authentic footage. By enabling traceability, broadcasters can defend against misinformation and build trust with audiences.

The Current Ecosystem
C2PA has emerged as a leading standard for embedding provenance information in digital media. C2PA’s framework allows for interoperable metadata and cryptographic signatures, making it possible for broadcasters, platforms and creators to verify the authenticity and history of content across systems. The standard is open and extensible, supporting a range of media types and workflows and even different industries beyond media.

Media industry adoption is accelerating, with several major broadcast organizations piloting provenance-enabled workflows in news and other content production. These initiatives often demonstrate clear potential benefits such as faster verification of user-generated content and enhanced viewer transparency.

Academic research is also contributing, with universities and consortia developing algorithms for media integrity verification and studying the impact of provenance on audience trust. Complementary efforts include the rights management standards and emerging watermarking technologies, which can work alongside provenance standards to further secure the broadcast value chain.

Broadcast operations are complex, built on decades of legacy technology and workflows. Integrating provenance requires compatibility with existing asset management systems, editing suites, and distribution pipelines. Many broadcasters rely on proprietary technologies and custom automation, making interoperability a significant challenge. Transitioning to provenance-enabled workflows often involves both technical upgrades and workflow change, which can slow adoption.

Editorial teams operate under tight deadlines and adding provenance checks can be perceived as a bottleneck. Provenance systems must be as frictionless as possible, automating metadata capture and verification without impeding creativity or speed. Infrastructure costs such as servers and storage are another concern, especially for smaller broadcasters. However, cloud-based solutions and open standards are helping to lower the barriers. “Provenance as a Service” will certainly arise at some point.

Effective provenance requires robust governance: clear policies on what data is captured, who can edit or verify, and how disputes are resolved. Broadcasters must balance transparency with confidentiality, especially when handling sensitive or proprietary material. Regulatory requirements are evolving, with new laws in the EU, U.S. and elsewhere beginning to mandate traceability for certain types of content.

In an era of media skepticism, provenance offers a powerful tool for rebuilding trust. Viewers are more likely to engage with and share content that is demonstrably authentic. Provenance-enabled broadcasts can display “verified origin” badges or offer interactive traceability features, strengthening audience confidence.

Regulators are increasingly scrutinizing broadcasters’ ability to verify the authenticity of news and public affairs content. Provenance systems can automate compliance reporting, reduce the risk of inadvertent misinformation and protect organizations from costly litigation.

Content provenance potentially opens the door to new business models. Distribution partners and advertisers may pay a premium for verifiable, tamper-proof media. Beyond compliance, provenance helps broadcasters manage strategic risks. In the event of a content dispute or crisis, having an immutable record of origin and edits provides legal and reputational protection. It also enables rapid response to misinformation, helping broadcasters get ahead of the narrative.

A Provenance-Enabled Future
I argue that the future is one where every piece of broadcast content is accompanied by a verifiable, machine-readable provenance record. Newsrooms collaborate across borders, sharing clips with rapid assurance of authenticity. Audiences can check the origin and edit history easily, while regulators and advertisers have confidence in the integrity of your content.

Provenance-native pipelines will be built on interoperable standards like C2PA, integrated into asset management systems and distribution platforms. Automation can handle the heavy lifting, capturing and signing metadata at every step without slowing down production.

Here is how to get started:

• Assess Current Workflows: Map out existing content creation, editing, and distribution processes to identify the best integration points for provenance.
• Pilot Provenance Solutions: Start with targeted pilots in high-risk areas, such as breaking news or user-generated content.
• Engage with Standards: Participate in industry groups like C2PA, contribute feedback, and help ensure interoperability with partners.
• Invest in Training: Educate editorial and technical staff on the value and operation of provenance systems.
• Establish Governance Policies: Define roles, responsibilities and escalation procedures for provenance verification and disputes.
• Monitor Regulatory Trends: Stay ahead of evolving compliance requirements. This is an area of rapid change.

Content provenance is no longer theoretical. As technologies like Generative AI rapidly improve, it’s necessary to counterbalance the risks in this fast-moving media environment. By investing in provenance today, broadcasters can secure their reputations and build lasting trust with audiences. The technology and standards are here and the time to act is now.

A recurring theme in this column is that change is the only constant in media technology and now we’re entering yet another inflection point. For the past two or three years, the conversation has been dominated by generative artificial intelligence (GenAI) large-language models, synthetic media and the promise (and peril) of machines that can create. But a new concept is emerging to take center stage: Agentic AI.

This shift is more than just a buzzword swap. It represents new thinking in how we consider automation and interaction. Where GenAI focused on content generation, Agentic AI is about delegation and communication. While the technology is still maturing, the trajectory is clear: agents are coming.

What Is Agentic AI, Really?
Before we go further into the world of agentic systems, it’s worth stepping back to clarify what we mean when we talk about “AI.” AI is not a single technology—it’s a spectrum of capabilities, each suited to different kinds of problems.

Over the past few years, the spotlight has been on generative systems—models that can produce text, images, audio or video based on patterns learned from large datasets. Attention is shifting toward something more dynamic: Agentic AI. These are systems that don’t just respond—they act. They can pursue goals, make decisions and interact with other systems or agents on behalf of a user.

A key distinction between AI and traditional automation lies in determinism: Traditional automation excels in deterministic environments, where inputs and outputs are well-defined and predictable. Think of a transcoding pipeline or a playout automation system. These are engineered for consistency and reliability.

AI, by contrast, thrives in nondeterministic contexts—where inputs may be ambiguous, incomplete or constantly changing, and where outputs are not always binary or fixed. This makes AI especially useful in areas like content personalization, natural language interaction or adaptive media workflows, where flexibility and learning are more valuable than rigid rules.

As we move into the agentic era, this distinction becomes even more important. We’re building systems that can operate in the gray areas, where human judgment used to be the only option. This is a significant evolution in how we think about automation. These agents may be powered by generative models, but they go beyond them by incorporating memory (context), planning and the ability to interact with other systems or agents. In some cases, it may even act without direct prompting based on what it knows about your goals.

The key point is this: Agents are not just tools. They are actors in a system, capable of making decisions, forming strategies and interacting with other agents in ways that mirror human delegation.

A New Media Ecosystem
To understand how agentic systems might reshape the media landscape, it helps to visualize the ecosystem they could create. That’s where the Agentic Model for media comes in.

At the center of the model is “Agentic Discovery and Communication.” This is the core function that ties everything together: the ability of agents to find, filter, personalize and exchange content on behalf of their human or organizational counterparts. This is a foundational concept: the emergence of a general agent communications plane-A layer that sits “above” the internet as we know it today. This plane would allow agents to interact, negotiate and transact with one another directly, without requiring constant human mediation.

Some envision a future where this agentic layer becomes the dominant interface for digital interaction—potentially superseding the traditional web. In such a world, websites and apps may become secondary to the agents that navigate the digital world on our behalf.

Surrounding this core are four key roles:

• A Creator Agent might help manage rights, optimize distribution or assist in content creation or personalization;
• A Brand Agent could autonomously place ads for a brand, negotiate campaign terms or monitor performance;
• A Personal Curator Agent would act on behalf of the consumer, filtering content, managing preferences and even negotiating access or pricing; and
• An Influencer Agent represents any entity granted the authority to shape or guide the behavior of other agents. This could include institutions, communities, regulatory bodies or even parents wishing to influence the curator agents of their children.

What emerges from this model is a vision of a media ecosystem where agents mediate nearly every interaction. It’s a shift from a platform-centric media model to an agent-centric one, where the locus of control moves closer to the individual or organization being represented.

Personal Agents and Data Ownership
Among the most transformative elements of the Agentic Model is the Personal Curator Agent—a digital representative that acts on behalf of an individual consumer. This agent doesn’t just recommend content: it negotiates access, filters noise, adapts to evolving preferences and potentially even manages subscriptions or monetization decisions. It becomes, in effect, a media concierge—one that knows your tastes, your values and your boundaries.

The need for such a capability has never been more urgent. We are rapidly approaching—if not already living in—what some have called the “dead internet,” a digital landscape increasingly saturated with AI-generated content, synthetic engagement and algorithmically amplified noise. In this environment, the content signal-to-noise ratio is getting worse. We will need agents to sift through the junk and identify what truly matters.

For a personal agent to be effective, it must have access to a rich and continuous stream of behavioral, contextual and preference data. That data might come from viewing history, social interactions, biometric signals or even inferred emotional states. In today’s media environment, much of that data is collected and controlled by platforms. But in an agentic future, the balance of power could shift—from platforms to people.

The Long Road Ahead
The pace of technological innovation often outstrips the pace of business model evolution. We’ve seen this before—file-based workflows were technically feasible long before they were widely adopted. Cloud infrastructure was ready years before media companies trusted it with their core operations. Even streaming, now ubiquitous, took more than a decade to become mainstream.

The core technologies—autonomous agents, large-scale models, distributed orchestration—are already emerging. But the real constraint isn’t technical; it’s organizational, economic and cultural. Business models will need to adapt. Rights frameworks will need to evolve. Standards for agent behavior, identity, and trust will need to be developed and adopted. And perhaps most importantly, people will need time to adjust to the idea of delegating meaningful decisions to machines. It’s a decade-long transformation, at minimum.

For media professionals, the message is clear: don’t wait for the future to arrive—start preparing for it now. Begin experimenting with agentic workflows. Rethink how your content is discovered, curated and monetized. Invest in data quality, interoperability and flexible infrastructure. And most importantly, stay curious.

Over the years, there has been one thing I have consistently written about: change. How are things evolving, and what can we do to get ready for these changes? In this column, I want to share my thoughts on the future, based on four decades of experience in guiding clients through uncertain times. Recently, many of us attended the NAB Show, which reminded us that the future in the media industry is always uncertain.

Change is unpredictable. If you look at the NAB Show agenda from 10 or 15 years ago, you will notice that some big topics back then, like 3D television, did not have the impact we thought they would. On the flip side, today’s hot topics, like artificial intelligence, barely got a mention back then.

No one can predict the future perfectly. To get ready for different possibilities, it is crucial to have a flexible business model, a talented team, and adaptable technology. These factors are all connected; a change in one affects the others. During my career, I have seen a lot of unexpected changes.

Oddly, innovative ideas would pop up quickly and shift our thinking but fully implementing them often took much longer than expected. For instance, moving from tape to file-based workflows took decades after it started.

Think about how things have changed over the last 40 years:

1985
When I first got into the industry, tape formats were the critical topic. Betacam had come onto the scene, but everyone was wondering if they should wait for Betacam SP or M2. We had computers, but they were just for text and not part of our workflow. Tube cameras were the norm, and edit rooms looked like space launch control centers. Cable TV was growing like crazy.

A camera operator needed a big, shoulder-mounted camera with a battery belt and a tape deck. Using the camera meant serious training and daily upkeep. Editing required a room that cost $20,000 to $30,000 for basic cuts and up to a $1 million suite (in 1985 dollars) for full effects.

Back in 1985, distribution channels were limited. Without network backing, you would have had to go through public access TV or distribute VHS tapes yourself, which made getting your content out there tough.

1995
We were now getting into file-based systems. Computers could make videos! We developed nonlinear editing and many other computer-based technologies and began to slowly—very slowly—get rid of tape in our facilities. We had solid-state cameras. Cable had exploded and the internet was just entering our consciousness.

2005
The digital revolution was underway in more than one sense of that word. Digital TV! Internet-based video services. Standardized HDTV was here, and in the U.S. we were trying to figure out how to deal with the move from 4:3 to 16:9 aspect ratios. Phones did more than make calls and were everywhere. They had screens that were playing video. User-generated content (UGC) was arising in online services. Cat videos were everywhere.

By 2005, that cameraperson had an integrated camcorder that was solid state with no need for separate battery and VTR. Consumer camcorders were in the market for under $1,500 with superior performance to the images of 1985 and it was a more stable and easier-to-use device. We now could edit on a computer and an edit suite was $5,000 with full mix and effects capabilities.

Think about what a difference that is—in 20 years, we made professional-quality media production capabilities available to anyone who could pull together single-digit thousands of dollars, down from nearly $1 million. Many more people could make content, and they were.

You had many more options for distribution. There were hundreds or even thousands of TV channels and you also had internet-based distribution options including early social media and streaming services. You had a greater chance of being seen by audiences.

2015
Change continues; now we are talking 4K, even early 8K! Streaming is all the rage and we are now immersed in augmented reality (AR) and virtual reality (VR). ATSC 3.0 was on the drawing board. Targeted ads were driving new revenue and the competitive landscape was changing with new media entrants.

2025
Any number of trends are critical today. Of course, there is AI, as has been discussed extensively in this column. But not just that—our business is being severely disrupted by streaming wars and the rise of social media, especially by Generation Z. We have mobile devices that can shoot professional quality videos and even edit them.

Today, all the capability you need to make professional content is in your pocket for under $1,000. You can shoot and edit high-quality content with professional-grade mix and effects with the device you carry around with you every day: From around $2 million in today’s dollars to less than $1,000 in 40 years.

You also have a tremendous number of options to get your content out; self-publishing is available to anyone on video-sharing sites and social media and the audience is now there. You can go “viral” if you have the right content and monetize without the same level of difficulty you would encounter just 20 years before.

2035
Can you predict what 2035 will look like?

I cannot. But there are some clear trends here we should recognize to help us look forward. There is a megatrend in the above: From 1985 to 2025, content production and distribution were democratized dramatically, from the few people who could make and distribute professional content to almost everyone.

I have every reason to believe that these democratization trends will continue. We have evidence that they will already. When it comes to production the obvious continuation is with generative AI. And there will also be new forms of distribution with Agentic AI playing a role. More on that in a future column.

Be Ready for the Future
Regardless of what future comes, we can be prepared for anything; we can act now. Here are some key truisms I have learned in architecting systems and workflows while engaging in transformation efforts from getting off tape to turning on AI.

What is most important here is to focus on business. All the technology and operational decisions are downstream of what the business is trying to accomplish. Of course, engineers are creative, too, and should become even more embedded in business to provide innovative ideas.

From a technological perspective, there are a couple of key things that will always be good to engage in—not only for your needs today, but to be ready for the future. You need to have a highly flexible infrastructure on which the business runs and a systems architecture that is flexible and easy to change. What does that mean in the modern era? Well, certainly commodity hardware and virtualization where possible, and cloud is appropriate in many cases to provide flexibility.

From a systems standpoint, you need the ability to manage and orchestrate best-in-class capabilities and operations. I started talking about Service Oriented Architecture nearly 20 years ago. You need to decompose your business requirements to workflow steps that fulfill those requirements; you then need to look at how to create services that can be reorchestrated or changed without breaking everything else. The term “Service Oriented Architecture” is not dead and applies today just as much as it used to. It’s worth getting familiar with, as AI does not deprecate SOA.

And then there is our data layer, which often lacks a unified source of truth or consistent dwata. We need to continue to engage in the endless project of making our data layer better through master data management, governance, metadata cleansing and even metadata generation. We need to do everything we can to have a clean pool of data our systems can rely on. AI only really works with good data to train and execute with.

I hope all this gives you comfort that you will not be out of work soon. It has been an incredible 40 years. I believe that in the next 40 years, we will have at least as much change to work through and enjoy. We all have much to do between now and 2035. Let’s get to it!

The phrase “content is king” was first coined in 1974 in the magazine industry and perhaps most famously repeated by Bill Gates in a 1996 essay.

Although it has been a long-accepted truism of our industry, I would argue that it really should be “curation is king” given that it is quality, not quantity, that matters most. In this piece I will argue that it is still true regardless and will be even more accurate in this era of generative artificial intelligence (Gen AI).

Fundamentally, my argument is that other aspects of the extended media become commoditized cyclically. The only differentiator that endures and is the greatest predictor of the success of a media business is the quality of its content. This has and will remain true from the traditional spaces like television and film and to the newest approaches in streaming, user-generated content sites, games and more.

This is because two things consistently become true no matter how rapid the pace of change gets. Both monetization approaches and technology commoditize and are not a long-lasting differentiator.

It is not that new business models do not arise or that there is no innovation. It is just that fast following is possible. It is easy to see that happened with the rise of cable networks or even subscription-based streaming direct-to-consumer. When a model is successful, it is followed by others and eventually all companies begin to have similar experiences and options for customers.

This has happened in each media era for all of media history. Currently, there is a rise (again) in ad-supported approaches such as free ad-supported television (FAST) or advertising-based video-on-demand (AVOD), and we are quickly seeing most parties adopt these options (again) for their consumers.

Content As Differentiator
Perhaps unsurprisingly, technology also commoditizes. Over time, any class of technology becomes similar. Because there is more than one way to approach any solution, this is not usually protectable by patents.

As an example, over time, nonlinear editing systems became broadly similar. This has been equally true of transcoding or content delivery networks (CDN). It has also been true recently about streaming platforms. They are all largely similar in recommendation engines, player capabilities, quality, etc. What differentiates them is the content itself.

This is why I argue that content companies hold far greater power than they may think with regard to the future of media. There are continued worries about the growing impact of tech companies relative to media and even concerns of the growth of AI-focused companies or capabilities in supplanting media. I think, conversely, that we are seeing the power of media as we watch AI companies start to license the content they train on.

In fact, this is what will be differentiating in the midterm. In the short term, there is plenty of innovation still happening in AI models, especially around architectures. But fundamentally these are algorithms and approaches with decades of existence and the core changes that users are seeing are due mainly to the sheer computing power and scale of data sets for training. There is already evidence of the decline in performance in the last year as new models are released and plenty of evidence that systems like LLM’s are much harder to scale than thought just a few years ago.

What will make for success for an AI model in the future? In my opinion, it will come down to available data sets and the proper curation and quality control of that data. In many respects, this is what content companies have been doing for decades. And content companies are in the actual business of creating new data (content).

The Value of Raw
As I mentioned in my September column covering the concept of model collapse (“Could AI Become its Own Worst Enemy?”), there appears to be no clear way to successfully use AI outputs or other synthetic data to train models to be successful in real-world media use cases. To avoid artifacts and increasing bias in the system, you need to continue training it with new and real data.

We are already seeing some licensing deals. What is most interesting about this is that what the models need is not just finished content; raw, unedited content is perhaps even more valuable for model training. A local news organization captures tens to hundreds of hours of raw video data each day that is reality-based and very valuable to train models. I would not be surprised at some point to see local TV stations make more money from licensing their content to others than direct revenue from shows.

I expect this trend in value to continue. It is likely the future will be more protective of content rightsholders. Whether at the individual creator level or major media companies, I expect there will be a series of legal precedents and regulations that protect against unlicensed access and other training that is not consented to.

What does all this mean now? It means that you should maintain confidence in the future of this industry, if you held any doubt. Secondly, it means that you should think carefully about curation approaches to content in whatever part of our space you are in. In a world where raw content may sometimes have more value than finished content, what do you choose to hold on to? This is a fascinating problem to think through and very situationally dependent.

Consider what you want to do to protect against bad actors who may access your data. Much of your finished content is visible in one way or another to the world and unlike direct piracy, using it for training is far less easy to detect. In addition to taking all the cyber and content protection steps you would normally want to take, consider the potential of “poisoning” your public-facing versions of content to hurt any unauthorized training while having a separate repository for training data authorized users can access.

AI-model poisoning is a fascinating topic worthy of its own column, but fundamentally it involves using techniques akin to what we currently do with invisible watermarks to corrupt the data in ways that genuinely fool a model regarding what it is “seeing.”

Watch Out for Poisoning
The best example of this in our space is a program developed by the University of Chicago called “Nightshade.” It can corrupt your imaging data in ways that are invisible or very subtle to humans while being so powerful as to make the AI think it is seeing a dog and not a cat, thus confusing the model when asked to generate a “cat.”

Note that this can be used by bad actors to poison your models if you access poisoned training data. A recent paper showed how these techniques could be used to make AI respond with false medical information so it would be wise to familiarize yourself with this potential vulnerability.

“Content is king” is certainly not the most original message, but with the advent of AI it is time to remind ourselves that this will always remain true. Focus on making great and engaging content and the rest will take care of itself.

Media technologists need to consider how to adapt to a future where artificial intelligence (AI) plays a larger role. What skills will the average media engineer require in the future? This is an important question for technology management, as they must determine how to upskill their existing workforce while recruiting new team members aligned with that future.

There are various media-specific technology topics to consider for future development, such as high dynamic range (HDR), new codecs and new methods of signal distribution. Additionally, there are fundamental skills to develop in both infrastructure and software development.

Basic Skills
Most importantly, the media engineer of the present has become a specific type of information technology (IT) professional. This has been evolving for more than 20 years. At this point it’s fair to say that a media engineer cannot be successful in their work without a professional level set of IT skills. While there are some cultural differences between traditional IT and media IT, media professionals are now expected to understand the basics of networking, storage and other core infrastructure.

In fact, it is critical that IT infrastructure skills be at an advanced level in media engineers. Media engineers should possess advanced IP networking skills and should seek significant design and build experience of complex networks, including SMPTE ST 2110 environments. Furthermore, it is important to have advanced expertise in on-prem and cloud storage and compute systems.

Knowing what it takes to at least build a small data center in your facility that can process high data rate live video should be the goal for anyone with more than a few years’ experience. Additionally, every media technology professional should possess at least one basic architect-level certification from a major cloud provider. Knowing how to relatively quickly spin up workflows in a cloud environment will be a skill in high demand going forward that is even more critical in AI workflows.

Next Generation
How does AI add to all the above basic skills? To discuss this, it is important to make some reasonable predictions on the near to mid-term future of AI in media. I think it is valuable to think of AI as essentially a new generation of software-based automation. It is my opinion that AI—as a concept—will fade into the background in media and will be embedded in tools that perform media functions (editing, visual effects, etc.) that we care about. In fact, this has been the case for more than a decade in the industry.

In fact, I expect the evolution of AI to follow a path already treaded by software systems in general in our industry. At first, there were large monolithic systems that didn’t interchange very well with other companies’ products except in finished and flat content—you would classically need to buy into a given vendor’s entire suite of products for it all to work together. This is the stage AI is at now. Today, most AI image generation systems output only a final image in a way analogous to the “print to tape” workflows of the 1990s.

In the future, I would expect standards for interchange and control to lead to the next evolutionary step, which is AI systems with more specialized capabilities that can pass components or data or instructions to another AI to handle. This is similar to the technology we had in the 2000s when we really began networking products from different vendors into single workflows.

Finally, we then will go through a set of phases like the “microservices” phase of the 2010s in which these AI models get very specialized and smart and reliable about doing a small scope of work and get orchestrated into agile workflows, perhaps even by AI-based orchestration engines.

If this future is accurate, then the skill sets that will be needed to be most effective in understanding, building, troubleshooting and managing these technologies in the future are the software-oriented skill sets seen in developers today.

Programming, Data and Prompts
It is important to develop a strong skillset in software development in at least one programming language. Doing so develops the logic and systems-flow skills that will be a part of systems designs that are heavily software-based. Python is very popular now and contains all the elements found in many languages, and so would be a good choice for beginners.

The fact that AI Large Language Models (LLMs) are generating code based on prompts does not take away from the need to learn a programming language. There are potentially significant limitations on LLM systems, which mean that for some time it will be necessary to understand code to debug and deploy quality systems.

Data-related skills are another area that will be critical in an AI-enabled future. A media engineer will need to be a data engineer/scientist too, which means gaining a fundamental understanding of databases and data structures as well as the methods to extract data such as Structured Query Language (SQL) and many others. Particularly relevant to AI are technologies such as vector databases and it may also be wise to refresh your knowledge of linear algebra as it is core to many generative AI technologies. Understanding the basics of statistics will also be important, including probabilities and other concepts.

It has been argued by some that prompt engineering has reached a level of complexity such that it is akin to a new kind of programming language. Regardless of the veracity of that statement, learning how to write good prompts for different systems is a skill that everyone, including technologists should really get under their belt.

As has always been the case, the media engineer is a multi-skill set engineer. Good media engineers have always known at least a basic amount about a lot of technical fields. That will remain true. But, more important than any of the technical skills listed above will be the continuing growth of softer skills—project management, change management, understanding business requirements and how to communicate. These are the skills that have been growing in importance in technical fields for decades and those skills will remain useful for your career—wherever it takes you.

The contemplation of artificial intelligence has a long history, arguably predating the invention of computers. Since the advent of modern computing, the hype cycle for AI has repeated itself numerous times. In 1970, Marvin Minsky, a pioneering figure in AI, was quoted in Life magazine saying, “In from three to eight years we will have a machine with the general intelligence of an average human being.”

This forecast did not materialize then and remains unfulfilled. Once again, the current AI hype cycle is approaching the "trough of disillusionment," but it is expected that we will soon reach a new "plateau of productivity" as the latest advancements are assimilated.

I have been somewhat taken aback by the rapidity with which the present cycle has moved past its peak, leading to more grounded expectations throughout the industry. Over the past couple of years, there has been extensive discussion about the hype and associated fears of AI.

In this column, I aim to delve deeper into the challenges inherent in AI technology itself. Many of these challenges exist independently of their application in media and can arise in any context. Although my focus will be on technological issues, it is crucial to acknowledge substantial non-technical concerns such as economic implications, rights and royalties, cultural transformations, and the legal and regulatory landscape surrounding the technology.

Within the scope of technological challenges, there are those where a resolution path is foreseeable and others where no clear solution currently exists. An example of a challenge with a visible path to resolution is electricity usage. We have multiple methods to generate electricity and can eventually develop the necessary infrastructure. Here, however, I will concentrate on a few challenges for which there is currently no evident way to resolve.

When talking about any form of technology, it's crucial to first define the term. In the context of AI, this definition is quite expansive, covering a range of underlying technologies. Commonly, AI now refers predominantly to Generative AI, particularly Large Language Models (LLMs) and associated technologies such as Generative Adversarial Networks (GANs) and others. This article will concentrate specifically on issues related to LLM technology.

Accuracy, Reliability and Quality
By now, we are all aware of some inherent issues in the predictive nature of LLM technology. One of the most prominent concerns is the tendency for these models to "hallucinate," producing results that contain objectively false, bizarre, or highly improbable information. While a certain degree of this can be beneficial, especially in creative tasks, it poses significant challenges in many situations.

Completely avoiding this issue is difficult. Architectures such as RAG (Retrieval Augmented Generation) aim to mitigate this by automatically supplementing the prompt with additional constraining data from traditional data systems like databases. Though this approach is promising, its inconsistent performance makes it difficult to rely on these tools for automated operations. For use cases that demand greater predictability and reliability, it is advisable to use historical automation technologies or trained human operators.

Artificial intelligence has demonstrated its proficiency in handling generic tasks, particularly those with abundant training data. Unfortunately, in creative fields, this often results in subpar content. Most data available to train AI content systems tends to be of low quality and lacks exceptional creativity.

Furthermore, due to the AI's inherent tendency to generate outputs that align with the "average" result, models typically produce quite unremarkable content. To date, AI has not consistently delivered high-quality, creative outputs, and achieving such results seems implausible without human intervention.

Model Collapse
Arguably, the greatest danger to the future of AI is AI itself. Numerous academic studies conducted over the past year have highlighted an apparent irony in our current approach. Essentially, the more effective AI becomes in serving our needs, the less beneficial it ultimately may become. This phenomenon is referred to as model collapse.

As illustrated below, LLMs (Large Language Models) are particularly compelling because they excel at predicting the next likely word, pixel, or other data points in their outputs. These predictions are highly accurate because they tend to average out the possible results. I previously mentioned this issue as a quality concern where the models generally generate "average" content.

A more intrinsic issue emerges from this statistical behavior. If we deploy AI in the real world and the volume of data produced by AI systems increases, the model becomes increasingly skewed by this averaging effect. Paradoxically, the more we use AI effectively, the more challenging it becomes to train it for future utility, leading us towards even more homogenized content.

Even with first-generation models today, you can observe a kind of uniformity in system outputs. As an experiment, try entering 12 different prompts about an elephant or another favorite animal into your preferred image generation platform. Then, use an image search engine to find real-world animal pictures. You'll notice a distinct similarity in the AI-generated images compared to the natural diversity found in actual photos.

The images below illustrate how rapidly these issues arise. After merely five generations of training on datasets containing AI images, the models start producing remarkably similar images. The greater the proportion of AI-generated data within the dataset, the more pronounced this problem becomes.

Introducing AI-generated data into the training set may lead to various other issues, especially if a broader variability is permitted to prevent uniformity. Rare events, like visual artifacts, could become more prevalent and rapidly degrade the dataset in unusual ways, as illustrated by the images below.

I compare the model collapse issue to the problems our industry has faced with analog generational loss. Just as we experienced increased dropouts and image fuzziness with successive generations of tapes, model collapse behaves similarly in its impact.

There are various techniques under discussion to prevent model collapse, but they all present their own challenges. One commonly suggested method is to train exclusively on data that hasn't been generated or influenced by AI. However, if AI effectively serves its purpose, such data will become increasingly scarce. This situation presents a significant irony not lost on this particular human!

Biased Training Data
One of the principal challenges with large language models (LLMs) lies in the inherent bias present in the datasets used for their training. These datasets are frequently sourced from the internet or private data collections. Considering that over 99.999 percent of worldwide information and experiences are neither online nor digitized, these sources introduce a significant bias in training.

The sheer volume of "data" consumed by an individual human daily far exceeds the amount available on the internet. And most of this is neither particularly compelling nor worth preserving and will never be in a form a computer can absorb.

Moreover, the simplest and most apparent realities are seldom directly published on websites, blogs, or other online platforms. Consequently, data is inherently biased towards what we find interesting enough to publish.

A pertinent example is the prevalence of smiling faces in image searches for people. Individuals typically post and store images where they are smiling, which skews the dataset. This bias results in generated images frequently depicting smiling individuals, which does not reflect real-life diversity.

Conclusion
I am inherently optimistic. Despite the challenges I've mentioned, I am enthusiastic about the innovative solutions being tested to address them. It is crucial, however, for media technologists to gain a deeper understanding of the fundamental workings of these technologies to effectively implement them within their organizations.

In my next article, I will explore the essential skillsets we need to develop over the next few years to ensure successful adoption.

The Olympics are more than just a sporting event—they are a showcase of human stories that inspire and captivate us. As media technology evolves, so does our ability to share these stories with audiences around the world. Artificial Intelligence is one of the technologies that is transforming how we experience sports media, especially at the Olympic level. AI can help us tell the story of each game or match, the historical significance of the event, and the ways we can keep fans engaged even after the event is over.

From Past to Present
Ever since the modern Olympics began in 1896 (Athens), people have been fascinated by the games. And with each new era, the latest technological innovations were used to bring the games to viewers across the globe. The first coverage of the event was through newsreels in theaters. Radio started with live coverage in 1924 (Paris), 100 years ago. You might be surprised to learn that television coverage began in 1936 (Berlin), with a kind of closed-circuit system that showed the games in public places near the stadium. It wasn’t until the1960 games (Rome) that the broadcast reached other countries.

The Olympic Broadcasting Services (OBS) was established in 2001 with the core mission to act as the host broadcaster for the games, delivering the sights and sounds to viewers all over the world. It started fulfilling that mission with the 2008 games (Beijing) and has been responsible for the main infrastructure and media related services for every Olympics since. OBS has been remarkable in its constant innovation with each event, maintaining quality and reliability during a time when media technology has changed a lot.

AI is part of this change. Depending on which specific AI technology we are talking about, we can say that AI was first used by OBS at the 2018 games (PyeongChang) where it was used for content tagging, recommendations, and language translation. It was also used in several other areas of the games, such as time recording systems and biometric analysis of athlete performance.

When in Paris…
According to OBS, the 2024 Games will be the most advanced yet in terms of technology with the two week event. The games will be fully produced natively in UHD HDR, along with immersive 5.1.4 sound using more than 1,000 camera systems and 3,600 microphones.

OBS will produce more than 11,000 hours of content and process more than 3,000 UHD and HD feeds within the International Broadcast Center. More than 80 different distribution feeds will be managed. The IBC facility covers about 40,000 square meters, a 13% reduction from the 2020 Tokyo Games held in 2020. A total of 36 different venue broadcast compounds will be supported.

Amazingly, OBS does this with only about 160 full-time employees. The core group expands to more than 8,000 people from more than 110 different countries during the games itself. This is an incredible organizational and technological achievement that only gets more sophisticated with each Games.

Of course, AI is also playing a bigger role at this scale of event production and distribution. AI will be used for auto-clipping of content and descriptive metadata tagging. The technology will be used to provide transcriptions and translations of interviews for journalists and to help them find content. It will also be used to provide captions/subtitles in real time for live coverage. Data gathered by various biometric and other sensors deployed through the event will be processed and provide unprecedented information to viewers, often in real time.

Perhaps most impressively, it will be used to create automated highlights for potential distribution to several different platforms, including those using vertical formats. These will be generated on demand at any level of interest from a county to a sport to an individual athlete and can be tailored to mood or many other factors that producers may want to consider.

2026 (Milan) and Beyond
AI technologies of all kinds will continue to play a role in allowing media companies to produce major live events with richer and more sophisticated viewing experiences. Even in the areas where AI is already playing a role, it is easy to see the potential for an even greater role. For example, clips and highlights packages could be generated for smaller audiences or even an individual viewer in a reasonably cost-effective way.

These highlight-generation technologies could include even more relevant stats and help producers and viewers find even more interesting “gems” hidden in the content. It can also help production teams fill time in between gaps in the action with some real-time context setting, or information about the sport, scores or other key story lines. It can bring real-time information with context directly to presenters and allow them to provide even more texture for audiences.

It can also help production crews keep an eye on what’s happening outside the venue and have a more 360-degree view of the “story of the games.” Audiences always want more and we’re reaching the limits of what’s feasible for a human production crew to do, so working AI into production and creative workflows will be crucial to bring better experiences. It will bring otherwise hidden stories to those who want to see them. AI is unlikely to ever be used to alter the “reality” a fan sees; instead it should serve as an enabling function to enhance our production of the content.

Fear of the potential impact of mal-intended generative AI creating fake or distorted content is an important concern that is being taken seriously by the IOC (International Olympic Committee), OBS, and broadcasters around the world. OBS has been explicit in its commitment to not tampering with the video.

One technology I would anticipate playing a role in coming games in this regard is C2PA (Coalition for Content Provenance and Authenticity), an open technical standard providing publishers, creators, and consumers the ability to trace the origin of different types of media. This will be critical in many areas beyond sport.

Between the Games
We can start thinking now about how AI will help with the story between the games. It might not be so obvious, but 17 days of competition isn’t enough to tell the lifetime story of how an athlete got there. When the flame goes out, preparation for the next games begins right away. AI tools that are looking beyond the content broadcasters generate and into social platforms and the broader “conversation” can help audiences experience the whole journey to the games and beyond.

The legacy for competitors, for cities, for fans who were present at these epic moments and watched on TV far away can be joined up using the careful application of AI to continue to activate fans throughout the cycle and maintain interest.

At its core, the Olympic games are a human event—an AI cannot compete in the games and can never tell the powerful stories in a way that will bond in a powerful, emotional way with viewers. AI’s roles are becoming clearer, however. In addition to providing capability for increased scale and sophistication, it is time to see AI as a technology to bring more of the “truth” to viewers. AI can assist us in bringing out powerful stories by helping us find and present more relevant and informational content than ever. l

John Footen is a managing director who leads Deloitte Consulting LLP’s media technology and operations practice. He can be reached at usmediamatrix@deloitte.com.

"Matrix” is a word with many different meanings, but they all share the idea of connecting different things into one. In our industry, we often use it to describe the switching equipment that connects signals to various destinations, like an SDI (Serial Digital Interface) “matrix.” 

I decided to name this column “The Media Matrix” because I will discuss how many new technologies are linked to media. Using technologies as varied as cloud computing, analytics and AI (Artificial Intelligence), media technologists are constantly changing our companies, the way that creators make content, and how audiences enjoy it. 

I will share this space with industry leader Karl Paulsen, where we will talk about the future of tech in media. We are ready to dive into any hot tech trend that pops up and share our insights. 

AI at NAB Show
Like the 2023 show, AI was clearly the buzzword at the NAB Show in April and we are going to zoom in on that first. There are many aspects of our business that will be impacted. From preproduction to distribution, every step of the media workflow has the potential to be transformed as AI evolves.

One of the first things that struck me in Las Vegas was how few booths had AI plastered all over them. Sure, there was a lot of buzz about AI at the show, but I thought every booth would have something akin to a flashing neon sign.

Maybe it’s because AI is not new to our industry, and we’ve learned to tone down the hype just a bit. I think technologists are looking for real examples they can apply to their operations now for practical use. There are some areas in media where AI is solid and ready to go—you can see for yourself how subtitling and language translation and even voice generation are working well. You can use AI to analyze content and add metadata to make it easier to find and use. You can use AI in post to improve images or create mattes. These and other use cases are worth exploring and implementing now.

At the conference sessions I managed to attend or speak at, there was also a lot of talk about the future possibilities of AI. While people often worry about fake content, in one session there were discussions about how AI can help bring more facts to journalists or directly to viewers in news and sports content genres. There were also discussions about how AI can help personalize content in digital distribution.

What I found most interesting were the number of seasoned technologists who were saying how we’ve seen this kind of disruption many times in media and how the “doom and gloom” predictions about how such disruptions impact creativity or jobs are often wrong.

AI’s Impact
In the next few months, we’ll have a lot of interesting topics to cover here:

Defining AI: What is AI? What are the different kinds of AI? How does AI relate to machine learning? What is the future of AI? What are the limits of AI? How do various types of AI work under the hood? Is the future the big models that get all the attention now, or smaller, more focused approaches to AI?

Workflows and Integration: What parts of the workflow in a media company can benefit from AI and how? How do we make AI work well with other technologies in our workflows? What standards are there for interacting with AI and what standards should media develop?

People and Management: How will AI affect jobs in the media and entertainment industry? What skills do technical folks need to learn to be ready for more AI in their work? How do we deal with the human side of change?

Infrastructure and Architecture: How do we set up our computing and storage layers to handle the heavy AI load in our environment? How do we design our data layer to improve the quality of data used to feed and train models? How do software application layers talk to AI models? Do we have enough power, cooling, and space to run all this stuff?

Cost and Financial: How much does AI really cost and is it worth it in different use cases? Are there ways to optimize the real-world costs of AI? How can we monitor the technology to give us efficiency insights?

Creativity: Can AI take over various craft or creative functions? How “creative” can we expect AI to be? What new content types or genres will AI enable in the media space?

Rights and Legal: What are the intellectual property issues with AI? How do we make sure that AI outputs won’t put our business at risk? What technological controls can we use to help protect the business?

Viewer Response: Do viewers really want a lot of highly personalized content? What about genres like sports where the shared experience is part of the value? As we collect more data about viewers how can we protect their privacy and ensure appropriate controls?

These questions are not all purely technical, but technology issues have been merging with business issues since media adopted IT practices. Today’s broadcast engineers are always asked to evaluate their technology investments through a business lens.

We won’t just talk about AI here. We have some slightly older tech trends still evolving like cloud and blockchain and many more. Interestingly, all these trends intersect with each other. For example, the C2PA (Coalition for Content Provenance and Authenticity) specification is a new approach for content provenance that uses watermarks and blockchain technologies to deal with critical issues like the risk of fake AI content in our ecosystem.

But let’s be honest. We are at the start of the hype cycle for AI, and it’s important to be well-informed on the subject for the many discussions that are coming and so we’ll focus there first.

Writing this column is something I’m very excited about. I’ve been in the media technology field for more than three decades, and it just keeps getting better and more fascinating.