When Bari Weiss tells a room full of CBS News journalists that their work isn\u2019t \u201cproducing a product enough people want,\u201d she\u2019s not just critiquing strategy; she\u2019s implicitly trashing a decade of actual reporting. If you\u2019re going to make that kind of claim about a legacy newsroom, the least a grown-up reporter can do is check the receipts.<\/p>\n\n\n\n
So I did. With AI. And I labeled it.<\/p>\n\n\n\n
The prompt wasn\u2019t mystical. It was blunt:<\/p>\n\n\n\n
This is the kind of question AI is useful for: not opinion, but finding and organizing<\/em> sprawling factual trails.<\/p>\n\n\n\n I tasked an AI research assistant with a specific, bounded job: compile a list of CBS News awards from roughly 2016\u20132026, grouped by year and program (60 Minutes, CBS Evening News, Sunday Morning, Face the Nation, 48 Hours, etc.). It pulled from award bodies, coverage, and network press releases: Emmys, Peabodys, Edward R. Murrow Awards, duPont-Columbia Awards, Scripps Howard, National Press Foundation, and more.<\/p>\n\n\n\n That\u2019s what AI is good at: chewing through volume. The judgment comes later.<\/p>\n\n\n\n Then I did the part AI can\u2019t do for me:<\/p>\n\n\n\n AI did the legwork; I did the skepticism.<\/p>\n\n\n\n The result is two different artifacts:<\/p>\n\n\n\n That separation matters. One is argument; the other is an evidence pack anyone can audit.<\/p>\n\n\n\n The PDF explicitly notes that the award compilation was generated with Perplexity. I\u2019m not hiding the tool; I\u2019m making it part of the method.<\/p>\n\n\n\n As I said in the previous post: because this is 2026 and not 1996, I did what any competent reporter should do: I used an AI research assistant to compile a decade of CBS News awards, and I\u2019m labeling that clearly so you can check the receipts yourself.<\/p>\n\n\n\n Ethics and AI groups keep telling newsrooms to do exactly this: be upfront about\u00a0what<\/em>\u00a0AI did,\u00a0why<\/em>\u00a0it was used, and\u00a0how<\/em>\u00a0humans verified the output. This is that, in practice.<\/p>\n\n\n\n Weiss\u2019s pitch to staff leans heavily on the idea that AI has turned \u201cbasic information into a commodity,\u201d and that only \u201crevelatory journalism\u201d can save them. My workflow takes that premise and flips it:<\/p>\n\n\n\n So when she arrives with a PowerPoint to tell Emmy-, Peabody-, Murrow-, and duPont-decorated journalists they\u2019re doing it wrong, I respond with something else: a transparent, AI-assisted audit of the record they actually built.<\/p>\n\n\n\n That\u2019s the point of this little experiment. It\u2019s not just about CBS; it\u2019s about what future-forward reporting actually looks like.<\/p>\n\n\n\n You don\u2019t just \u201chave takes.\u201d You run the numbers, you document the method, you use the machines, and you sign your name to both the argument and the process.<\/p>\n\n\n\nStep 2: Use AI for the grunt work<\/h2>\n\n\n\n
Step 3: Verify like an old-school reporter<\/h2>\n\n\n\n
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Step 4: Separate evidence from commentary<\/h2>\n\n\n\n
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Step 5: Disclose AI use on purpose, not as a confession<\/h2>\n\n\n\n
Step 6: Turn transparency into a counter-argument<\/h2>\n\n\n\n
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What I\u2019ll do differently next time<\/h2>\n\n\n\n