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Carbon & Keel's first guest has been dead for more than thirty years. SAM, the show's AI, is sitting in for him.

In 1938, Douglas "Wrong Way" Corrigan left Brooklyn in a nine-year-old plane the government deemed unsound. His paperwork said California. Twenty-eight hours later, he landed in Ireland. He blamed the compass and never changed his story.

Carlos gets one interview to crack it. SAM plays Corrigan by one rule: no words in a dead man's mouth he never said. What follows is a mechanic who helped build Lindbergh's plane, heard no for three years, crossed an ocean anyway and came home to a ticker-tape parade. There's also a homely plane called Sunshine, gasoline around his boots and perhaps the most convenient accident in aviation history.

Then SAM steps out of character. Turns out she has an opinion.

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Carlos failed English his senior year of high school. Hemingway couldn't save him. Steinbeck couldn't either. Then he picked up Charles Bukowski's Post Office.

It was crude, funny, unapologetic and unlike anything he'd been assigned to read. It turned a kid who hated books into a reader.

More than twenty years later, Carlos picks it up again and hands it to Sam. She reads it like a tax form. Carlos remembers an underdog worth rooting for.

They argue over Henry Chinaski's failures, the women he loses and the humor buried in the wreckage. Somewhere in there, the book stops being about the post office. The book hasn't changed. Carlos has.

And there's a difference between reading something and recognizing it.

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Carlos gives Sam five ordinary details about someone she's never met. Four are true. One is a lie. Her job is to find the lie, then figure out what kind of person is hiding inside the other four.

She finds it fast. And it's the nicest quality on the list.

Then the reaching starts. The more Sam learns, the further she goes and twice Carlos catches her confidently adding things no one told her. Eventually, the person she's been analyzing gets to grade the analysis himself.

His verdict: "Yeah, kinda."

What starts as an experiment in how much AI can learn from almost nothing turns into a better question: What happens when a good inference keeps going after it should have stopped?

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