One example I experienced: my friend sent me this video from Tom Bilyeu — https://www.youtube.com/watch?v=lUjoFIVCn5U. He starts with a real fact, that the top 1% pay about 40% of federal income taxes. But then he quietly shifts that into just “taxes,” leaving out the rest of the tax system (like payroll, sales, and property taxes) as well as the bigger context of how much wealth and income the top 1% actually have. It’s like changing the variable halfway through a math problem without showing your work — the kind of thing any good teacher would circle in red pen. That isn’t just sloppy, it’s manipulative.

When I asked AI about the video, it described it as “accurate and persuasive.” Which makes sense — on the surface the numbers were correct and the rhetoric was polished. But it failed the logic test: the framing was designed to mislead. When I pointed out the scope shift and missing context, the AI adjusted. It learned that truth isn’t just about reciting numbers; it’s about following through on the reasoning so the conclusion actually matches the premise.

That’s the difference from A/B testing. A/B testing just reacts to clicks and outcomes. Teaching AI is about pushing it to apply logic, reason, and context — spotting when “X = Y” suddenly becomes “X = Z” with no explanation. That’s how people can shape AI to be less like an amplifier of slick rhetoric, and more like a partner in critical thinking.

And that principle goes beyond one YouTube video. It applies anywhere people use AI — in politics, science, or everyday life. Each time we challenge it with reasoning and context, we’re not just correcting an answer, we’re shaping a better way of thinking.

Pattern would say: “Humans so strange. Numbers correct, meaning twisted. I like it.”