In this video, I show you how to quickly and easily switch from ChatGPT (or any other LLM provider) over to Claude without losing all those precious memories you've built up. Give it a watch if you're one of the many making the switch to Claude! Enjoy :)

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.

Most teams do not have a meeting problem, we have a clarity problem that produces meetings. Meetings are often the interest payment on decisions we did not structure, documents we did not write, and expectations we did not make visible early. When clarity is missing, we compensate by gathering people in real time to sort it out. AI can help us reduce meeting hours, but not by “summarizing meetings better.” The bigger time win is preventing unnecessary meetings in the first place by making work clearer before we sync. When we do that, we shrink time-to-decision, reduce follow-up loops, and protect deep work. ------------- How Meeting Debt Builds ------------- Meeting debt is like technical debt. We take a shortcut today, “Let’s just talk it through,” and we pay for it later with compounding costs. Each meeting spawns another: a pre-meeting to align, the meeting itself, and a follow-up to clarify what we decided. Add in context switching and the time it takes to regain focus, and the true cost is much larger than the calendar block. We often schedule meetings because we are trying to resolve ambiguity live. The agenda is vague, the goal is unclear, and the decision criteria are not defined. People show up with different assumptions and different levels of context. Then we spend half the meeting getting everyone to the same starting line. Here is the common micro-scenario. A stakeholder asks, “Where are we on this?” The team has progress, but it is scattered across Slack, email, and someone’s head. Instead of writing a crisp update, we schedule a meeting. The meeting produces more discussion than clarity, and now we need another meeting to finalize a decision. The work did not move forward, it just moved around. AI does not remove the need for human conversation. It reduces the time we spend using conversation to compensate for missing artifacts. When we bring clarity into the work earlier, meetings become shorter, fewer, and more decisive. ------------- Insight 1: Meetings Expand to Fill Uncertainty -------------

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!

We do not lose the most time doing hard work. We lose time repeating ourselves. We re-explain the same context to teammates, to new hires, to stakeholders, and to our own tools and templates. Then we act surprised when cycle time stays high and rework keeps showing up. A shared prompt library is not a “nice to have.” It is an operational asset that turns repeated thinking into reusable leverage. When we build it well, we stop paying the setup cost every time we open a task. We get time back through faster starts, fewer revisions, and shorter handoffs. ------------- The Hidden Cost of Starting From Zero ------------- Most teams have recurring work that looks unique on the surface but is structurally the same underneath. Weekly updates. Client emails. Meeting agendas. Project briefs. Job posts. Performance notes. Training docs. Risk reviews. The categories are predictable, but we treat each instance like it is brand new. That is why context becomes the bottleneck. Someone begins a task, then spends 20 minutes remembering what “good” looks like. They hunt for last month’s version, copy it, patch it, and hope they did not miss a key detail. They ask someone else for examples. They send a draft that is close but not aligned, and then they get feedback that could have been avoided if we had a shared baseline. This is not just wasted writing time. It is wasted coordination time. Every time we start from zero, we create more back-and-forth. People react to style differences, missing sections, or unclear “definition of done.” Rework rate rises because the first draft is not wrong, it is inconsistent. Inconsistent work triggers extra review. AI makes this problem more obvious because it can generate so much so quickly. Without a shared library, we end up generating new versions of the same thing, each slightly different. That creates confusion and more time spent arguing about format and tone instead of substance. A prompt library is how we standardize the starting line. Standardizing the starting line is one of the fastest ways to shorten cycle time.