Intelligence agencies have long collected more data than they could analyze, being rate-limited by human translator and analyst capacity. AI provides nearly infinite, cheap cognition to process this data, making comprehensive surveillance of all unencrypted communications economically and logistically feasible for the first time.

A significant threat in the AI era is not just disinformation, but mass data obfuscation. Adversaries will 'maliciously comply' with transparency laws by burying truth in petabytes of AI-generated 'slop.' This creates a 'Red Queen's race' where truth-seekers must constantly innovate to keep up.

The NYT leverages AI not for writing articles, but for enhancing human-led reporting. Its internal teams build tools to find patterns in massive datasets, like sifting through millions of pages of the Epstein files or analyzing satellite imagery. AI accelerates discovery, allowing journalists to tell new kinds of stories.

California's CalMatters uses an AI called 'Tip Sheet' to analyze public records of politicians, including speeches, votes, and campaign contributions. The AI flags anomalies and potential stories, which it then provides exclusively to human journalists to investigate, creating a powerful human-AI partnership.

Contrary to traditional journalism where narrow hypotheses are key, AI-driven investigations yield better results from broad, open-ended prompts. This approach inverts the old workflow, allowing the AI to surface unexpected connections from the data rather than merely confirming a reporter's preconceived idea.

Many media outlets misapply AI by trying to make it write articles, which results in terrible quality. The technology's true journalistic value lies in its unprecedented ability to analyze vast troves of legal and financial documents, uncovering stories that were previously impossible for humans to find.

As the pace of AI-driven change and information generation accelerates, actors like journalists and courts may be unable to keep up without using AI assistants. This creates a dangerous dependency, forcing them to rely on potentially biased systems controlled by the powerful entities they are supposed to hold accountable.

The economic disruption of AI in journalism isn't about incremental cost savings. Its real power is enabling stories that were previously 'straightforwardly infeasible' to produce, regardless of budget. These are investigations that would have required an impossibly large team of specialists, costing tens of millions.

While most local government data is legally public, its accessibility is hampered by poor quality. Data is often trapped in outdated systems and is full of cumulative human errors, making it useless without extensive cleaning.