Created from a single voice note with Agent Craft
X (Twitter)
An engineer I know hasn't taken a full weekend off in four months.…
An engineer I know hasn't taken a full weekend off in four months. Not because his company is demanding it. Because he can't justify stopping. That's the thing nobody predicted about AI. The whole narrative going in was simple: AI arrives, automates the tedious stuff, workers get their time back, everybody wins. The beach house fantasy. Shorter hours, same output, better life. It made perfect sense on paper. Except that's not what's actually happening. Take software engineers as the clearest example right now. AI has made them somewhere around 100x more productive. That's not a rounding error. That's a different category of capability entirely. You'd think that means the work gets done faster and people log off early. But the opposite is happening. Engineers are sleeping less. They're working weekends. They can't switch off. Why? Because the opportunity cost of rest just went through the roof. If you can ship in a day what used to take a week, and your peer is doing the same thing, then stopping to relax isn't really rest anymore. It's falling behind. And if you're a type A player, that calculation runs in your head constantly. Why would you coast when you can create something genuinely impressive in the time it used to take just to set up the scaffolding? This is the counter-intuitive part. AI doesn't reduce urgency for high performers. It raises it. The people who thought AI would create mass unemployment or at least a gentle four-day-week for everyone missed something about human nature. Specifically, about the kind of humans who actually drive output in any field. They're not optimizing for leisure. They're optimizing to get ahead, to build things, to see what they're capable of. Give them a faster engine and they don't slow down. They find a bigger road. So instead of the unemployment wave that the doomers predicted, what we're actually heading toward is a productivity explosion alongside a group of people working harder than ever. The shift isn't from employment to no employment. It's from constrained productivity to something much larger, with the people who move quickly to take action capturing almost all of the upside. This pattern isn't going to stay in software. It'll move into marketing, law, finance, design, anywhere that type A people are already pushing hard. The tool changes. The behavior doesn't. The engineers staying up late aren't victims of AI. They're the ones who figured out what it actually means to have a 100x multiplier in their hands. That's not a tragedy. It's just not the story anyone was telling a few years ago.
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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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- FacebookMost SMB executives know exactly what they want to say. They built the business. They have the ideas, the perspective, the stories. The problem isn't the thinking. It's the sitting down. So here's the real question for the practitioners in the room: is the bottleneck actually time, or is it something else? Because "I don't have time to post" and "I don't know how to turn what's in my head into a polished post" are two very different problems with two very different fixes. Which one actually describes you or your exec team?
- X (Twitter)Let's be real for a second. The debate about AI content has collapsed into two camps and both of them are missing the actual point. Camp one says AI content is killing authenticity. Delete it all, go back to writing everything by hand, protect the craft. Camp two says AI is a productivity win, ship faster, scale harder, stop being precious about it. Both camps have recruited loudly. Both camps are wrong in the same way. The sameness is the problem. Not the AI. I've joined this party myself. I've seen what happens when a brand runs everything through the same model with no real voice input. Every sentence lands with the same weight. Every paragraph breathes at the same pace. The cadence is so consistent it becomes invisible. Readers don't always know why they stop reading. They just do. But the answer isn't to throw the tool out. The answer is to stop feeding it nothing and expecting something back. Here's the uncomfortable version of this: most people who complain about AI content sounding generic are using it generically. They're typing a prompt and accepting the first output. That's essentially asking a contractor to build a house with no blueprint and then being surprised when it looks like every other house on the street. The DNA of your content has to come in before the output comes out. Voice, opinion, specific experience, things you've actually said out loud to real people. If none of that goes in, generic is what you get. That's not an AI problem. That's an input problem. So no, I don't think the binary is useful. It's not "AI good" vs "AI bad." It's whether you're bringing enough of yourself to the process to make the output worth reading. Which is the harder question: are you actually putting your real voice in, or are you hoping the model will invent one for you?