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Xian's avatar
1dEdited

When you are building, you are trying to close the gap between your taste and your execution. Only by building can you actually move toward where you want to be. Once you start, failing is inevitable. The struggle and the friction are not detours, they are where real learning happens.

AI shines at removing low value friction: repetition, formatting, and mechanical steps that add little insight once they are mastered. But building mental models, debugging when things break, decomposing systems, and making decisions under constraint are what create long term value. Those are the parts you still have to earn yourself.

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Gregory Meisel's avatar

Dan, I hope you don't mind, but I have some thoughts on your thesis here.

First, you're absolutely right about specific knowledge being the moat. That Naval quote is spot-on, and I think you've correctly identified the problem with commoditized information products.

But I think there's a dangerous assumption underlying this piece: that rapid iteration and short business model lifespans are inevitable rather than symptoms of competing in commoditized markets.

I've spent the last decade building precision timing infrastructure and AV control systems. Before that, 20 years in live sound engineering. The principles I learned mixing concerts in the '90s are still relevant today. The broadcast timecode fundamentals haven't changed in decades. When I'm debugging PTP synchronization issues, I'm applying knowledge that compounds over years, not iterates every quarter.

Here's my concern with the "build for tomorrow, not next year" mindset: if your business model expires in 2-3 years, you don't have a business—you have a job with extra steps.

The creator economy conflates disseminating information with creating value. You can wrap knowledge in AI chatbots, but you can't prompt-engineer the kind of specific knowledge that comes from 10,000 hours of actual practice. My brother is a retired rock drummer. You could feed every book on timing into an AI, create the world's best "drum coach" chatbot, and it still wouldn't capture what he knows from four decades behind a kit.

The real moat isn't speed of iteration. It's depth of expertise that takes years to develop and can't be commoditized by the next platform or tool.

What am I missing? Where do you see deep technical expertise fitting into this model?

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