The internet had a massive impact on the media industry, as the reporting of news became a commodity few are willing to pay for. The creation of content, but especially the sharing of content became a zero cost game. Initially, many publishers were slow to recognize the internet’s impact on their business model as distribution and the classifieds and ad worlds split – or what Ben Thompson calls the “Great Unbundling”. But in the coming age of generative AI, endless synthetic content and misinformation – there might be an opportunity for traditional media companies to regain their reputation as a 4th branch within a democracy. And make some money along the way.
The realization that most people do not want to pay for the “core” product called news came late. This oversight resulted in a missed opportunity to collect primary user data—a cornerstone for the digital advertising business that tech giants like Facebook and Google capitalised on. These platforms, leveraging vast amounts of user data, were able to offer targeted advertising solutions that were both more effective and appealing to advertisers, significantly eroding the ad revenue streams that publishers had relied on.
Furthermore, the rise of social media platforms transformed how and where people consumed content, especially among younger audiences. This shift not only fragmented audiences but also reshaped content consumption habits, leaving traditional publishers grappling with how to retain relevance and revenue in a rapidly evolving digital landscape.
With a few exceptions, most media companies struggled to transition into a subscription model and became addicted to clicks, as barriers to entry in media are very low (everyone can write a blog, share his opinion and news on social media) and media is widely available for free, it became a commodity very few are willing to pay for.
Generative AI (or Gen AI for short) certainly further democratises the creation of all sorts of media – we could call it a “digital production revolution”. At Remagine Ventures we know that all too well, as we invest in these new tools (Hour One to create synthetic videos, Munch to help distribute short-form videos on social media, KwaKwa to create short-form educational videos, Playo to create games within minutes, Quiiiz to bring the Trivia game-play to mobile). In a few years, most media on the internet will have been created synthetically, especially video with its high production costs, monetisation potential and dominance to grab attention as a medium compared to all other media.
While I believe that there is an incredible amount of economic opportunity AI can unlock for everyone by democratizing the creation, distribution and monetization of content – I also believe that for the first time in a long time – new opportunities exist for traditional media publishers around the world.
Let’s dive into a few of these:
Value of Cultural and Contextual Data for LLMs
The $60m deal between Google and Reddit highlights a crucial trend: the hunger for rich, diverse, and culturally relevant data to feed and refine Large Language Models (LLMs). Media companies and publishers, being at the forefront of content creation, possess and constantly create new high-quality repositories of such data, not just in text but in various multimedia formats. This positions them uniquely to leverage not just their archives, but also their new content on an ongoing basis to partner in developing more contextually aware and culturally sensitive AI models. This collaboration could extend to creating localized models that better understand and generate content for specific regions or communities, enhancing the relevance and reach of AI applications.
Counter-Positioning
As generative AI makes content creation increasingly accessible, distinguishing between human-made and AI-generated content will become more challenging, and will lead to an over saturation of synthetic content. This scenario underscores the value of authentic, original content, especially one that reflects deep human experiences (real ones), expert insights, or high production quality. Media companies and publishers can capitalise on this by positioning their content as premium offerings, building trust and turning it into currency.
Production costs of content will continue to fall drastically – Case study Video
Video is a great case in point. One of our strategic investors is using HourOne.ai (disclosure: a Remagine Ventures portfolio company) to create synthetic video news around sports. The quality of the synthetic characters and of the video is high enough to monetise via pre-and-post rolls while the cost of producing these videos is extremely low. Using Munch (disclosure: a Remagine Ventures portfolio company) , these videos can then be automatically clipped, subtitled, formatted and distributed on social media channels – creating engagement and additional revenue.
Example:
One-to-One Engagement Through GenAI
Generative AI opens the door to unprecedented personalisation in content delivery and interaction. By harnessing AI, publishers can move beyond traditional segmentation and personalisation techniques to develop truly individualised experiences. This could manifest in chat-bots that allow readers to interact and dive deeper into the content, or reach new and younger audiences. Especially 1:1 conversations are interesting, changing the relationship between media and the consumer from 1:many to 1:1. This deep personalisation not only enhances user engagement but also opens new avenues for subscription models, advertising in our new privacy-restricted media world and services tailored to the unique interests and needs of each subscriber.
For example, the Financial Times just launched a Generative AI assistant that enables subscribers to ask questions about all the content published in the last 20 years
Innovative Advertising Products with GenAI
Generative AI’s capability to create personalised content extends to advertising, where media companies can leverage AI to craft highly targeted and contextually relevant ad experiences. Beyond just personalisation, AI can help readers find relevant local goods & services. It could help bring back some of that classified ad revenue that was lost.
Ethical and Responsible Use of AI in Media
If traditional publishers want to own “trust” they will also have to be very transparent about their use of Ai technology. This includes transparency about AI’s role in content creation, respecting copyright and intellectual property rights, and ensuring the accuracy and fairness of AI-generated content. As we are heading towards a world full of misinformation, media companies have an opportunity to lead by example, setting industry standards for ethical AI use that builds trust with their audience and safeguards.
Expand into Gaming
This industry has traditionally been expensive to enter. Creating a game usually takes months, even years if it’s Triple A quality, and more often than not does not create the anticipated engagement. With Gen AI this is about to change, as production costs for games will come down drastically, reducing barriers to entry. A good example of a publisher adopting a gaming strategy is the NYT. “Readers” are spending more time on NYT Games than anything else.
Objective Journalism as a Competitive Advantage in the Age of GenAI
In an era where content can be generated en masse and tailored to echo the reader’s existing beliefs, the real opportunity for newspapers and media companies might lie in establishing themselves as bastions of objectivity and balanced reporting. This approach can serve as a counterbalance to the polarising, highly politicised content that often dominates social media feeds and niche news outlets. AI can also help with this mission, by:
- Enhancing Fact-Checking with AI: GenAI can dramatically scale up the capabilities of fact-checking teams, sifting through vast amounts of data to identify inaccuracies or misleading statements faster than ever before. By positioning themselves as factually rigorous, media companies can attract readers seeking reliable information, thereby distinguishing their brand in a crowded and often dubious information landscape.
- Balanced Reporting Powered by AI: Utilizing AI, media companies can ensure that their reporting covers multiple facets of a story, presenting a range of perspectives. AI tools can analyze content for bias, suggesting areas where additional viewpoints could enrich the narrative. This approach not only helps in building trust with an audience seeking unbiased information but also educates and informs by exposing readers to a broader spectrum of opinions.
- Interactive Platforms for Public Discourse: Media companies can leverage GenAI to create moderated, AI-driven forums and platforms where readers can engage in healthy debate and discussion. These platforms could use AI to enforce guidelines, highlight insightful contributions, and ensure a respectful and constructive exchange of ideas. This not only fosters community but also reinstates the media’s role as a public square for democracy.
Harnessing Specialised Magazines for Vertical LLM Development
Everyone knows that in AI, garbage in (when it comes to data training) means garbage out. But is the opposite true as well? Media companies can create their own smaller, specialised LLMs to boost vertical content creation. Leveraging the specialised content and data from these magazines, vertical LLMs can be tailored to offer highly customised and personalised experiences for professionals and businesses. Whether it’s generating industry-specific reports, providing tailored advice, automating customer service with industry-specific knowledge, or curating personalised learning and development content, the possibilities are vast and varied.
- Depth and Expertise in Niche Domains: Specialised magazines often curate content that delves deep into the nuances of specific fields, whether it’s technology, biology, real-estate, healthcare, finance, or any other industry. This content is not just informative but is written by experts who bring years of experience and depth of understanding to the topics. For AI developers, this represents a gold mine of terminologies, insights, case studies, and contextual knowledge that can be invaluable in training LLMs to understand and generate industry-specific content with a high degree of accuracy and relevance.
- Strategic Collaborations and Ethical Use: To maximise these opportunities, media companies behind specialised magazines could explore strategic collaborations with AI developers focused on creating vertical LLMs. Such partnerships could involve licensing arrangements, data-sharing agreements, or co-development projects aimed at creating next-generation AI tools tailored for specific industries.
These are just a few opportunities we see for traditional media companies. It’s up-to-them to exploit these opportunities and it involves a cultural shift we did not talk about here. To do that media companies have to abandon their “click-bait” strategy that even publishers like the NYT often pursue and later on have to apologise for. Serious publishers have to stop behaving like social media platforms. To stand out and demand a premium, media companies will have to go back to doing actual journalistic work. They have to embrace AI and new tools, try them out, fail and try again. One of our investors and leading media companies in Europe, Axel Springer, is setting up new templates to do just that. It will be a crucial job to safeguard our democracies.
Talk to us at Remagine Ventures if you are a founder thinking of disrupting these industries and also if you are an executive at a traditional media company! Remagine Ventures specialises in pre-seed and seed investments at the nexus of AI and interactive media and entertainment in Israel and the UK.
Managing Partner at Remagine Ventures, focused on the intersection of tech entertainment and commerce. Passionate about eSports. Originally from Germany, spent years in the US and now in Israel, where I live with my family.
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