Happy Monday, everyone. This one was filmed on the way up my mountain. Still a long way from the top, out of breath, nothing rehearsed. I turn 58 this year. Thirty plus years into this work, and somewhere on that trail the same voice still shows up three or four times to tell me I could stop right here and nobody would think less of me. I gave up trying to silence it a long time ago. What I do with it instead is what I get into in the video. Learning AI works the same way. We didn't grow up with this stuff. We're the ones cutting the path for everyone who comes after us. So of course it feels uncomfortable. Of course the doubt creeps in. Of course the old way of working keeps calling you back to it. That isn't a sign you're failing. It's a sign you're climbing. And the only thing that actually gets you up the hill is what I was doing on that trail. One step. Then the next one. Without quitting. Focus of the Week: 👉 Don't Miss Your Mountain So here's what I'm asking this week: name the mountain you keep promising yourself you'll climb someday, and name the one step you can take toward it before Friday. Drop it in the comments 👇 Have a fantastic Monday, Dean

The instinct to ship something slightly below your own standard, telling yourself it can be revised and improved later, isn't new. What's changed is the specific dynamic AI has introduced around this instinct: because AI has made revision feel cheap and fast, it's become significantly easier to justify shipping something imperfect now on the assumption that fixing it later will be quick and painless when the time comes. The problem is that later rarely comes with the same reliability the assumption implies, and the accumulated debt of small, deferred fixes represents a growing, largely invisible cost that AI's speed makes easier to accumulate, not easier to actually pay down. ------------- Context ------------- Before AI meaningfully accelerated the revision process, the decision to ship something imperfect carried a natural weight, because everyone involved understood, at least implicitly, that fixing it later would require real, deliberate effort: reopening the work, remembering the original context, doing the actual revision work manually. This natural weight discouraged excessive deferral, because the cost of "later" was genuinely felt as significant enough to warrant getting things closer to right the first time whenever reasonably possible. AI has changed the felt cost of "fix it later" considerably. Because revision with AI assistance can often happen quickly once you actually sit down to do it, the anticipated cost of deferring a fix feels low, which makes the decision to ship something imperfect now, with the intention of fixing it later, feel like a low-risk trade-off. The problem with this logic is that it assumes the "later" moment will actually arrive with the same reliability the low anticipated cost implies. In practice, deferred fixes compete for attention against new, more pressing priorities that continuously emerge, and a significant portion of items deferred with the intention of fixing them later simply never get revisited at all, regardless of how quick the actual fix would have been if it had happened.

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