Google's new Chrome browser is "AI everything." Meta's smart glasses promise to revolutionize how we cook and browse. But here's the problem: they want to automate the things we actually enjoy doing while creating solutions for problems that don't exist.

In this episode, I explore why 95% of AI initiatives fail to deliver value, and why that's actually a good thing. Drawing inspiration from Catch-22's observation that "while none of the work we do here is very important, it is important that we do a great deal of it," I argue that the current AI bubble serves as a crucial filter—helping us separate genuine innovation from opportunistic noise.

The Pleasure Theft Problem: Why AI tools that browse the web for us miss the point entirely

• "When I search for something, I'm not just seeking an answer; I'm seeking the journey to that answer."

The Wrapper Economy: How most AI startups are building unnecessary layers over existing tools

• "They're solutions in search of problems, built by teams who've mistaken the impressive capabilities of foundation models for proof that any application built on top of them will be equally impressive."

The Successful 5%: What the companies actually getting value from AI are doing differently

• "They solve clear, measurable problems... work behind the scenes, augmenting rather than replacing human judgment... and integrate seamlessly into existing workflows."

The Great Filter: Why all this "waste" is actually necessary

• "Every failed AI gadget teaches us something about the difference between technological capability and actual utility."

"The real breakthroughs won't come from the companies promising to revolutionize everything. They'll come from the teams quietly solving specific problems with appropriate tools, building on solid foundations rather than chasing the latest trend."

The AI bubble will deflate, but the signal will emerge from the noise. It always does.