The homepage was the most important digital asset that news publishers had for decades. It was the first thing readers saw; it influenced traffic patterns and provided editors with a place to feature the day’s most significant stories. AI is altering this equation. The most valuable aspect of a publisher to an AI company might not be today’s homepage, but all the content that’s been published over 20, 50 or even 100 years.
That transition is one of the reasons that archived journalism is increasingly significant in the broader content arena. In a digital world where the capacity to organize, retrieve, and interpret vast amounts of information is becoming increasingly useful, platforms and specialists like risha.ai are emerging. AI systems require up-to-date information, but also a historical context, reliable source material and vast archives of structured language. All three can be found in news archives.
Yesterday’s News Has Become Training Material
After a newspaper article is published, it generally loses a lot of its business value.
Traffic falls. The story scrolls down the home page. Yet another round of news. AI alters that lifecycle.
While an article about an election from 15 years ago may not have the same levels of conventional traffic these days, it can still be a source of names, dates, organizations, places and explanations that can help an AI system understand a large topic.
Scale that up to millions of articles and a publisher now has a valuable dataset.
The established news archives are something very useful: information that has already been vetted. Usually articles have been researched, written, edited, categorized and timestamped.
Which makes them quite unlike random collections of text pulled from all over the web.
Archives Give AI Something It Often Lacks: Context
AI systems can handle vast amounts of data, but comprehending the sequence of events can be challenging. News archives can be of assistance.
The article could be contemporary and describe an energy company’s strategy. An archive can show its mergers, leadership changes, regulatory disputes and previous business models. It is important in the context of that history.
It is the same with politics, technology, sport, finance and nearly all other types of news.
When an AI assistant asks ‘why?’ about something, it requires more than the latest headline. It must know what has gone before it.
Decades-old publishers have timelines to thousands of companies, people and institutions.
It’s hard to replicate in a hurry.
The Homepage Is Valuable for Hours, the Archive for Years
Traditionally, the economics of publishing have been based on the immediate.
When people want to know what’s happening now, they’ll visit sites with breaking news. That sense of urgency is at the core of homepages, push notifications and social posts.
However, the business life of these assets may be brief. An archive is different; its value accumulates.
Each research paper contributes to a pool of knowledge. Not every story is a newspaper’s today’s story when it has been around for 50 years. Thousands of interconnected records explain how today’s story became possible.
That compounding model could prove more valuable to AI companies than a fleeting moment of homepage attention.
This may be the beginning of a shift in thinking for publishers regarding their legacy content.
Publishers Are Starting to Think Like Data Owners
With the advent of generative AI, media businesses are reassessing their assets. Articles are not the sole property of a publisher.
It can have decades of structured data about companies, governments, markets, cultural trends and major events. That’s a potential licensing asset.
Publishers can now more often negotiate about access to their archives, rather than technology firms taking it indirectly. The discussion thus shifts away from copyright enforcement.
It’s a data economy issue. When a technology company needs the right information to enhance search accuracy, answer queries, or develop bespoke AI solutions, a well-maintained archive becomes invaluable.
The answer may be significant for the largest publishers in the future.
Specialist Journalism Could Become Particularly Valuable
Not all archives are created equal. While a century of general news reports is helpful, specialist collections might be even more appealing for some AI applications.
Corporate reports are available in financial newspapers and in many other sources. Technology publications report on product releases and industry changes. Legal publishers preserve cases and regulatory developments. The sports outlets keep in-depth records of players, competitions and results.
These collections already break down complex topics in easy-to-understand terms.
That’s the kind of content AI systems need when they are supposed to provide expert responses rather than general overviews.
So a smaller, more niche publication might have a more valuable AI data set than its number of readers would indicate.
AI Could Change the Economics of Old Journalism
Archived articles have long been ‘second-class’ assets.
Publishers focused on creating the next generation of readers, since advertising and subscriptions were based on them.
AI brings in another possible customer: the machine that requires understanding yesterday.
But that doesn’t mean that current journalism isn’t important. Indeed, the value of the archive depends on its ongoing maintenance by publishers who submit accurate new reporting.
However, this alters the old-new content dynamic. Today’s homepage is soon forgotten by the public. That is not the case in today’s reporting.
It is in a well-maintained archive and part of a growing information resource that can be searched, licensed, and reused for decades.
In an AI-powered media economy, publishers might find their biggest digital asset is right under their noses: the homepage.
