As someone who worked for 40 years in ad-supported media, the realities of today’s business are brutal, and that was true before generative artificial intelligence, which is accelerating the underlying economic trends.
In a nutshell, here’s the business model problem: Many media businesses are no longer primarily “storytelling organizations” but traffic monetization systems. As writers we act as storytellers. But whether that is harnessed financially can often be unclear or unworkable.
Since advertising “cost per thousand” impressions have collapsed over the last few decades, so have media entity revenues, bringing huge cost pressures to the forefront.
Platforms such asGoogle, Meta and X have captured distribution and pricing power, driving many former independent or smaller entities out of the market.
So revenue per article must be less than the cost of human labor to produce that content. So automation becomes a survival move.
Machines can often produce content abundance, especially when the content has high structure. Humans ideally produce scarcity value when lots of insight, interpretation or “meaning” are required. But generative AI is making inroads there, as well.
The economics of content production therefore favor using machines to produce mass content at scale, when possible, while humans have the edge only where scarce, specialized or trust-critical content is involved.
Basically, it is all about marginal cost and associated revenue upside. Investigative reporting might, in some cases, have very-high revenue potential, though it is rare. Original analyses have high production cost, and might have moderate revenue lift.
Breaking news, data-driven news or automated summaries invariably have low revenue upside. So the choices are fairly simple: automate what does not produce reasonable amounts of revenue, reserving human roles for the more-complicated content that will be a relatively-small part of total content production.
Most user-generated content business models follow a somewhat-similar, but “flipped” pattern. UGC platforms follow the same economic logic (abundance versus scarcity, automation versus humans), but the roles of humans, machines, and “content” are different.
Abundant, low-value content is automated, while scarce, high-value content is human-produced.
But users supply the labor for abundance, instead of journalists. The platforms automate selection, amplification, and monetization, so scarcity comes from attention, status, and trust.
That flows from the differences in media models. Media companies pay to produce content and then monetize the content.
UGC platforms get their content for free and monetize behavior around that content. In other words, the real product is engagement, not content. So curation is more important than content supply, which is, for all intents and purposes, unlimited. Attention is the source of scarcity.
Generative AI accelerates the content-creation processes, which becomes even more abundant and lower cost. But the algorithms still curate. So UGC platforms will optimize for watch time; shares; comments or return visits.
So algorithms will favor emotionally activating content; identity-affirming content or controversial content. That might be likened to “commodity” news, the sort of stuff that, in a professional media context, is structured enough to be automated.
Top UGC creators, in terms of revenue potential, often provide insights on what matters, what to ignore or how to think about something. They often focus on meaning, which is the same “scarce human” function in professional media.
Ironically, AI increases competition for attention, but raises the premium on human scarcity. That happened with journalism after the internet; music after streaming or photography after smartphones.
Abundance also tends to make authentic human insight more valuable, not less, even if it remains rare, as surfaced by the algorithms.
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