The Real-World Video Market: Do Machines Now Care More Than Humans Do About What’s Real?

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​For years, the internet rewarded spectacle above almost everything else. The stranger the clip, the more likely it was to spread. Viral culture turned reality into entertainment, and entertainment into a race for attention.

Now we are entering a strange new phase. Artificial intelligence is flooding social feeds with synthetic images, generated video and fabricated moments that often outperform genuine footage in terms of reach and engagement. At the same time, some of the world’s largest AI companies are searching for something much less glamorous: real-world footage.

For those creating and selling authentic video content, this could mean a fundamental shift in the market for their output, where consumption shifts from human to robot.

A New Source Of Demand For Real-World Video

At my company, we spend a lot of our time dealing with the complexities of authentic video. For 14 years, we have worked with publishers, broadcasters and content creators to source, verify and license real-world footage from around the world. Historically, the value of that footage came from editorial demand. A remarkable clip might be picked up by a major broadcaster or become the video that "broke the internet" that day.

Today, the economics are changing.

Traditional publishers still care deeply about trusted content, but many are operating under intense commercial pressure, limiting the budgets they can spare for content. Meanwhile, AI companies are emerging as some of the biggest buyers of real-world footage anywhere in the market.

That is happening because AI systems need real-world video to learn how the world actually is from an audio-visual point of view.

The public conversation around AI often focuses on generated content. We see fake celebrity images, impossible wildlife videos and photorealistic scenes that never happened. Yet behind the scenes, the companies building the next generation of AI tools are placing enormous importance on verified footage of the real world.

In many cases, they need huge quantities of it.

AI’s Hunger For Authenticity

One example is computer vision systems used in security. These tools are designed to analyze live CCTV feeds and identify events in real time. A system might need to recognize somebody tailgating through a secure door, attempting a break-in, loitering suspiciously or suffering a medical emergency.

To train those systems, developers need authentic footage of real people behaving naturally in real environments. Genuine footage is essential for these purposes because human behavior contains endless unpredictability. A staged incident frequently fails to capture the tiny details that matter.

The same principle applies in robotics.

While futuristic demos might tend to grab headlines—Sony’s ping-pong-playing robot being a case in point—arguably the more commercially important challenge is teaching machines to perform everyday tasks. Things like cooking, cleaning, laundry and even just navigating a room safely or picking up objects without damaging them.

To make these tasks possible, robotics companies are training systems on point-of-view footage showing people carrying out ordinary activities. Often the camera is mounted on the body to capture precise movement and coordination. Again, authenticity is critical.

Then there are the huge foundation models powering generative AI itself. These systems consume huge amounts of video and audio data in order to understand how humans move, communicate and interact within a range of environments in the world around them.

Ironically, the rise of artificial content is increasing demand for authentic content. For those who create video, this has created a surprising new opportunity.

From Viral Lottery To Dependable Revenue Stream

In the old viral economy, success was unpredictable. A creator might spend years chasing the elusive clip that captured global attention. Today, there is growing demand for footage that is far less dramatic but no less in demand. A person preparing food in a kitchen. Somebody walking through a supermarket. Natural conversations. Everyday life captured clearly and consistently.

For experienced creators with large archives, the commercial potential can be substantial. Some contributors who built careers licensing editorial footage are now generating meaningful new revenue streams through AI licensing deals. In certain cases, years of archived footage suddenly have renewed value because they contain exactly the kind of authentic human activity AI developers need.

This shift also raises a bigger cultural question about trust.

Social media platforms are still struggling to distinguish clearly between authentic and synthetic content, and they have been slow to regulate, with synthetic content still all-to-often being passed off as real.

In many environments, engagement remains the dominant priority. Yet the organizations building advanced AI systems increasingly understand that poor quality or misleading data creates poor quality outcomes.

What this means is that, ultimately, reality still matters, and therefore so does content provenance.

News organizations learned long ago that trust is built slowly and lost quickly. Broadcasters like the BBC, Reuters and AP invest heavily in verification processes because audiences, businesses and the global news industry depend on them to separate fact from fiction. Increasingly, AI developers are confronting the same challenge.

For my company, verification has always involved a combination of technology and human judgement. Metadata, upload locations, source histories and behavioral signals all help establish whether footage can confidently be called authentic. Over time, trusted networks emerge. Reliable contributors build credibility. Suspicious patterns become easier to spot.

That infrastructure, originally developed for publishers, is proving equally valuable in the era of AI.

The Machines Seeking Truth

For years, technology platforms helped create an online economy where the currency was clicks and the fuel that drove them, humor and outrage. This opened the door for the tsunami of AI-generated slop that is flooding our socials currently.

Now it is the AI builders who are driving demand for video content that can confidently be called "authentic."

Social media spent years rewarding whatever captured attention fastest, whether it was real or not. AI, paradoxically, is rewarding the people who can still show the world as it truly is.​

Check out the original article on Forbes.