What if Spotlight could be done in an afternoon? Not because the story mattered less, but because AI is here and has gotten better at reading, understanding and making sense of documents. In the film Spotlight (which I was watching over a vacation recently), a team of Boston Globe journalists slowly uncover systemic abuse inside the Catholic Church. Their process is deliberate and analog. Courthouse visits, whispered interviews, cross-checking names in dusty directories are all part of the drama that the movie offers. The impact of the story came not just from what they found, but how they found it.
A lot has changed from the timeline of the movie. A lot of what took them months, activities like identifying patterns, analyzing archives, connecting hidden dots can now be done in a few hours with the right agentic deep research AI system. Give a well-trained model access to court filings, local news reports, and church records, and the same connections could surface before lunch. The difference isn’t in the truth itself. It’s in the speed, scale, and scope of what’s possible.
We’re already seeing these kinds of deep research workflows in use, but mostly inside national security and defense. A recent AP report described how Tulsi Gabbard thinks AI can save money and free up intelligence officers. These tools are already real. They’re being deployed to track influence networks, surface geopolitical threats, and monitor narrative shifts. The first organizations to benefit from AI-powered investigation have been agencies like the DNI and companies like Palantir.
But this technology doesn’t have to stay inside the intelligence community. The same underlying capabilities like structured retrieval, temporal reasoning, long-context synthesis can and should be used for civic discovery. There are thousands of Spotlight-level stories waiting in court transcripts, procurement data, environmental reports, police records, and regulatory filings. The public interest layer of AI is massively underbuilt. The problem isn’t whether we can find these stories. It’s that we haven’t made it easy or efficient for the right people to look.
That’s where the opportunity is. AI won’t replace the hard parts of journalism, and it shouldn’t. The judgment, context, emotional depth, and moral clarity still come from people. But the overhead like searching, sorting, sifting can be reduced dramatically. We are shifting from a world where reporting is limited by human bandwidth to one where the bottleneck is insight and intention. The work becomes less about chasing down facts and more about asking better questions.
We’ve already built AI copilots for code, design, writing, and productivity. But the real question now is this: Who is building the copilot for investigative journalism?