I'm seeing it too, but there's a distinction I think matters: AI isn't replacing the thinking, it's shifting where the bottleneck is. You mention systems are self-improving and code quality has increased dramatically. But the constraint isn't execution anymore. It's judgment at scale. When AI collapses build time from weeks to hours, the new bottleneck becomes staying current with what's actually changing. You need to know what competitors shipped, what research dropped, what patterns are emerging across 50+ sources continuously. Generic ChatGPT can't do that. It doesn't know what YOU care about. It starts from scratch every time. The real question is how do you build personal AI that learns YOUR priorities and filters the noise? That's where the leverage is now.

Excited for the future :)

> You need to know what competitors shipped, what research dropped, what patterns are emerging across 50+ sources continuously. Generic ChatGPT can't do that.

You're saying that a pattern recognition tool that can access the web can't do all of this better than a human? This is quintessentially what they're good at.

> The real question is how do you build personal AI that learns YOUR priorities and filters the noise? That's where the leverage is now.

Sounds like another Markdown document—sorry, "skill"—to me.

It's interesting to see people praising this technology and enjoying this new "high-level" labor, without realizing that the goal of these companies is to replace all cognitive labor. I strongly doubt that they will actually succeed at that, and I don't even think they've managed to replace "low-level" labor, but pretending that some cognitive labor is safe in a world where they do succeed is wishful thinking.