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Eric Schmidt got booed at a university commencement address last week…
Eric Schmidt got booed at a university commencement address last week for talking about AI. I find that fascinating. Not because the students were wrong to feel something, but because of what the booing actually reveals. The backlash isn't really about AI being bad. It's because AI is becoming impossible to ignore, and that's uncomfortable. Discomfort looks for an outlet. Booing is an outlet. But here's what I think people are missing. There's a concept called the K-shaped economy. A K has two lines coming off it: one going up, one going down. The rule of thumb is simple. If you're AI native, you're on the upline. If you're not, you're on the downline, and you probably wouldn't even know it. The students booing Schmidt? The people talking about AI slop dismissively and calling for the whole thing to stop? They're on the downline. And feeling superior about it doesn't change the direction of travel. This moment of backlash will pass. It always does with technologies that become genuinely unavoidable. What gets left behind is a clear divide: the people who got serious about AI adoption and the laggards and resistors who mistook cynicism for discernment. The laggards might feel like they're holding the line on quality or authenticity. I think they're getting things wrong. Being dismissive of AI right now is a bit like being dismissive of the internet in 1999 because most websites looked terrible. The websites got better. The people who sat it out got left behind. You don't have to love AI. You don't have to think every output is brilliant. But if you're still in the "AI slop" camp and that's your complete position, it's worth asking honestly which line of the K you're actually on. If this resonates, drop a comment. I'm curious whether you're seeing this split in your own industry.
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- LinkedInI used to think testing marketing assumptions was straightforward. Run an A/B test, look at the numbers, move on. Turns out I was testing the wrong things. The assumption most SMB executives never question is whether their content is actually reaching the right people at all. They obsess over the message, the offer, the copy. Meanwhile, the distribution is quietly broken and nobody's checking. Here's what shifted my thinking. A single 45-second voice note generated 45,000 impressions on LinkedIn. Not a carefully crafted campaign. A voice note. When I saw that, I had to reset my clock on what good content methodology actually looks like. The old testing model assumes effort correlates with results. You pour time into a polished post, you expect reach. But that assumption doesn't hold anymore. The executives who produce thought leading content consistently, even imperfectly, are outperforming the ones who wait until they have something "worthy" to say. So what should you actually be testing? Test the format before the message. A rough voice-recorded insight posted Tuesday morning might outperform a professionally written piece that took three hours. If you haven't compared the two, you don't know. Test the source, not just the copy. Content that sounds like a real person, with a specific point of view and a specific situation behind it, performs differently than brand content. Most companies have never isolated that variable. Test whether your executives are visible at all. If the only person posting is someone in marketing, you're running one experiment when you could be running ten. The methodology failure I see most often isn't bad testing. It's testing inside assumptions that were never examined. Be prepared to pivot when the data tells you the assumption itself was wrong, not just the execution. That's where the real gains are.
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