The future of disinformation is no longer about creating better content. It is about teaching machines what to retrieve, summarize and recommend. In the AI era, the editor matters more than the article.
A story no longer has to trend. It has to be retrieved when a large language model (LLM) constructs the answer. The most important audience in the information environment is no longer human. If an AI assistant becomes the primary gateway to information, influencing what it retrieves becomes more valuable than influencing what millions of people read directly.
The Hugging Face Model Hub, the top repository worldwide, tracks over 2.9 million total machine learning models. Many models are for wonderful uses, ranging from research universities to new private sector companies built to solve problems. Meanwhile, more than 130 active national sovereign initiatives exist in more than 60 countries, according to the Center for a New American Security (CNAS) Sovereign AI Index. Every one of these models becomes another editor with its own worldview, training corpus and retrieval strategy.
As Bob Dylan reminds us, “For the times they are a-changin’”.
Countries are in pursuit of a foundational model (cognitive sovereignty) that will provide its own historical context, experts, values, national interests and more.
Different versions of reality will emerge, not because people disagree, but because different models were taught to retrieve, prioritize and reason differently. Not unlike media outlets as they evolved, just with a completely different scale.
Our Focus
The old world was about content, distribution and amplification. The new world is about training, retrieval and reasoning.
We must have the expertise to explain the mental frameworks machines construct before we see an answer to our query.
Our slide decks will cross out the “attention economy” and replace it with the “cognitive economy.”
We will remind ourselves that during the social media era, an adversary would flood the zone with thousands of fake articles and accounts to amplify a narrative. In the AI era, the objective changes. Rather than convincing one person at a time, adversaries will increasingly seek to influence the system that answers everyone.
Perspective is also important. Printing presses made publishing a reality. Radio introduced the broadcast message. Television opened up reach to mass audiences. Social media democratized who could have a voice. And AI now changes who decides what we receive.
Our Preparation
We will need to expand our remit and add expertise in training data provenance, retrieval indexes, embedding systems, model guardrails, agent memory, citation chains and reasoning architectures. AI engineers will become important parts of our team, if not already so.
The decade ahead will introduce AI models and agentic systems that decide what billions of people see. Agents will continuously search, compare, negotiate, monitor and decide for us. Humans may never initiate the request, but the agent will know what to do. That’s a different information ecosystem. We must learn how to track its development accurately and efficiently, so we are in-step or a step ahead on each new innovation of importance.
AI models will cite other AI models who cite other AI models. Over time, the original source may disappear entirely behind layers of machine summarization. The citation survives, but the human reporting becomes increasingly distant.
The editorial model will change as quickly as it needs to. How do we keep up with changes in the perspective of a model on a key topic and why it occurred?
Bad actors will optimize less for search engine optimization (SEO) and increasingly for generative engine optimization (GEO), engineering content specifically to influence what AI systems retrieve and cite.
We will need a new intelligence platform that tracks all publicly accessible LLMs and all innovation in places like Hugging Face, so we can see patterns earlier across the world. Imagine tracking hundreds and then thousands of LLMs in real-time. We still care about what happened, who said it and the rest of the 5Ws, but increasingly, it will be meaningful to know how Claude, Gemini, ChatGPT, DeepSeek and other models summarize and frame key messages.
These platforms will help us as we develop skills to understand training data integrity, how retrieval systems are poisoned through Retrieval-Augmented Generation (RAG) attacks, and how agent memories are manipulated.
Invisible Persuasion
When the audience is the machine, our efforts shift from how to protect the population to how we analyze and influence the infrastructure that reaches us.
Media literacy taught us to evaluate what people published. Machine literacy teaches us how to evaluate how machines constructed the answer.
The era will have many names, I’m sure, but one that resonates with me is “invisible persuasion.”
Unlike propaganda, machine-led information thrives on invisibility. It must appear ordinary, mundane and just do its job.
The next generation of AI will continue to quietly remove the human being from both ends of the media system. It is becoming the audience, and it is becoming the editor. And it is doing both at once.
It is also succeeding in building trust in humans.
SparkToro and Datos Group found that 60% of US google searches ended without a click in the first four months of 2026.
The same person who once clicked through to read is increasingly staying put while a machine goes and reads for them. The Reuters Institute expects search referrals to nearly halve over the next three years.
A Pew Research Center report showed that users clicked a source cited inside an AI summary just 1% of the time (900 US adults, 68,879 google searches).
Trust is migrating from the publisher to the summarizer. Our learning used to include more friction – a competing headline or comments we disagreed with. Now, we get a clean answer without friction.
How this impacts our judgement is a question we’ll study for many years ahead.
Conclusion
The printing press democratized publishing. Search democratized discovery. AI is centralizing editorial judgement again, this time inside machines.
The new editors are not confined to newsrooms. They include model developers deciding guardrails, publishers licensing training data, platform owners determining retrieval rankings, governments building sovereign AI models, open-source communities releasing foundation models, and enterprises curating the knowledge bases their AI agents consult. Editorial power is becoming distributed across the AI stack rather than concentrated in traditional media organizations.
The organizations that understand how machines learn, retrieve, reason and remember will shape the next information environment.
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