Perfection is expensive, but ambiguity is even more expensive. Most teams do not lose time because they aim too high. We lose time because we do not agree on what “done” means, so we keep revisiting the same work. A clear Definition of Done is not bureaucracy, it is a time strategy that protects cycle time, reduces rework, and speeds up decisions. AI amplifies this truth. When we generate faster drafts, the bottleneck becomes alignment. If “done” is unclear, we simply produce more versions, faster. If “done” is clear, we produce better first drafts, faster, and we get time back instead of creating more noise. ------------- The Time Leak We Keep Normalizing ------------- We have all watched a simple deliverable turn into a multi-week loop. Someone submits a document. A reviewer says, “This is not what I expected.” Another reviewer asks for more detail. A stakeholder wants it shorter. Someone else wants it more formal. The author revises, resubmits, and the cycle repeats. We call it collaboration, but often it is a missing agreement. The real issue is that we asked for “a brief,” or “a summary,” or “a plan,” without defining the job the artifact must do. That vagueness creates handoff latency. People cannot evaluate quickly because they do not know what standard they are evaluating against. So they revert to preferences. This is also why meetings expand. When a deliverable is unclear, we schedule a sync to “align.” The meeting becomes a debate over expectations that could have been written in two paragraphs. That meeting leads to changes, which leads to more review, which leads to more time lost. A Definition of Done is how we stop paying this clarity tax. It gives us a shared finish line, which shortens time-to-decision and prevents expensive rework. ------------- Insight 1: “Done” Is a Contract, Not a Feeling ------------- Most teams treat “done” like a vibe. We know it when we see it, and we assume everyone else does too. That assumption is the source of wasted hours.

In this video, I go over the various updates and releases from Google and Anthropic, discusses the upcoming AI hardware releases from Apple and OpenAI, tests out a frankly creepy demo of a live interactive AI avatar, and more. Enjoy!

You failed. Okay. Take a breath. First, let’s just acknowledge something. You were in the arena. You put something out there. You risked looking stupid. You risked it not working. That already puts you ahead of the majority of people who are still “thinking about it” or “getting ready.” Failure has a way of messing with your head. It makes you question yourself. It makes you wonder if maybe you’re not cut out for this. But almost every time, it’s not about who you are. It’s about what you did. There’s a big difference. When something doesn’t work, it’s usually a strategy issue, a clarity issue, a focus issue, or just not enough reps. It’s rarely an identity issue. But if you make it about your identity, you’ll shrink. If you make it about the approach, you’ll grow. So instead of asking, “What’s wrong with me?” ask, “What can I learn from this?” What broke? What did I assume that wasn’t true? Where did I hesitate? Where did I rush? If you paid the emotional price of the failure, at least get the lesson out of it. That’s where the value is. The only real danger isn’t failing. It’s quitting. It’s deciding that this one outcome defines you. It doesn’t. It defines a moment. And moments can be adjusted. Sometimes you don’t need more effort. You need a different angle. Sometimes you don’t need a new dream. You need more reps. Sometimes you just need to stay in the game longer than the discomfort. Failure isn’t the opposite of success. It’s the path to it. And once you stop being afraid of it, once you realize it can’t actually hurt you unless you let it stop you, you start playing differently. You start playing to win instead of playing not to lose. That’s the shift. So let me ask you this...What did your last setback teach you and what are you going to adjust because of it?