In this video, I did something a little special, as I was out of commission for a week due to surgery. Instead of skipping the week in AI news, we put some of the best modern AI tools to the test to see what we could create. So I'm proud to present our guest host AI Igor, who will only be filling in this week while I rest my voice. AI Igor covers the results of the testing we've been doing on the top models for the past week, talks about the new Copilot Cowork coming to Microsoft 365 users, discusses the disappointing release from Luma with Uni-1, and more. Enjoy this special edition and I will be back next week!

A lot of teams think AI adoption has to begin with a major initiative. They assume it needs a strategy deck, a sweeping rollout, a big announcement, or a fully formed transformation plan before anything meaningful can happen. But in practice, that is rarely how real momentum starts. Most teams that are getting value from AI are not winning because they began bigger. They are winning because they began smaller. They found one repeated task, improved one workflow, saved one useful prompt, tightened one handoff, and turned that small gain into a repeatable system. That matters because small systems reduce time-to-value much faster than big ambitions do. ------------- Big intentions often create slow adoption ------------- When teams talk about AI in broad terms, the conversation can sound exciting but still go nowhere. People discuss possibilities, future use cases, competitive pressure, and all the ways work might change. But because the scope feels so large, no one knows exactly where to start. That is one reason big transformation language can actually slow adoption. It creates pressure without giving people a clear path. The topic becomes important enough to talk about, but too abstract to use. And when something feels abstract, it usually stays separate from daily work. This is where many teams lose time. They spend weeks discussing AI at a high level while the real opportunities are sitting in plain sight inside recurring tasks. A bloated workflow. A repeated handoff. A first draft that always starts from scratch. A review process that keeps creating the same delay. None of these problems require a grand transformation to improve. They require a usable system. AI becomes valuable when it stops being a topic and starts becoming part of how work moves. ------------- Tiny systems create faster time-to-value ------------- A tiny system is not complicated. It is simply a repeatable way of using AI to reduce friction in a task that happens often enough to matter. That could be a prompt template for weekly updates, a checklist for reviewing drafts, a workflow for turning notes into a client follow-up, or a standard structure for summarizing research.

Let me talk about something that comes up for a lot of people when they’re trying to grow… How do you know if it’s resistance…or if it’s a sign you should quit? Because when things get hard, the mind starts talking. “This isn’t for me.” “Maybe I picked the wrong path.” “This shouldn’t feel this hard.” “Maybe I’m forcing it.” What I’ve learned after decades of building businesses, taking risks, and watching people succeed or quit is that resistance shows up when you’re about to do something that matters. Not when you stay comfortable. Not when you play small. Not when you go through the motions. Resistance shows up when you try to grow. It looks like procrastination. Overthinking. Doubt. Fear. Starting and stopping. Talking yourself out of the very thing you once said you wanted. And the crazy part? The more your life is about to expand…the louder resistance gets. So before you decide to quit, ask yourself: Is this really wrong for me…or is this just the part where growth gets uncomfortable? Because most people don’t fail because they chose the wrong path. They fail because they listened to resistance at the exact moment they were supposed to push through. Stay in the fight. Your next level might be closer than you think.

We often talk about time as if it only lives on the calendar. We count meetings, deadlines, deliverables, and hours worked. But some of the most expensive time loss in modern work never appears in a schedule at all. It lives in the background, in the mental effort required to keep unfinished work active in our head. That is where AI can become surprisingly powerful. Not just as a tool for output, but as a way to reduce the hidden time tax of carrying too much unresolved thinking at once. ------------- Where Time Leaks Before Work Even Starts ------------- A lot of people assume they need better time management when what they really need is less mental carrying. We do not just spend time doing work. We spend time remembering what needs to be done, revisiting half-formed ideas, holding open loops in memory, and trying not to lose important details before we have a chance to act on them. That overhead is real work, even if it does not look productive from the outside. Think about a normal day. We may have a proposal to finish, a follow-up email to send, a team decision we still need to make, a new idea for a process improvement, and three conversations that require thoughtful replies. Even when we are not actively working on those things, part of our attention stays attached to them. We keep mentally rehearsing, “Do not forget that point,” or “I need to circle back to that,” or “There was a better way to explain that.” That constant background processing drains energy long before the task itself is completed. This is one reason people end a day feeling busy but strangely unfinished. The issue is not always a lack of effort. It is that attention has been fragmented across too many mentally open loops. The brain becomes a storage system, a reminder system, and a drafting space all at once. That creates invisible cycle time. It slows the path from thought to action, from task to completion, and from idea to value. In that sense, the real time leak is not just workload. It is unexternalized workload. The more work we hold in our head, the more time we lose to friction, context switching, and re-entry. AI matters here because it can help us move thinking out of our head and into a form we can work with faster.

A lot of people assume AI hesitation is a knowledge problem. They think the people not using it yet simply do not understand it well enough. But that explanation misses something important. Many smart, capable people are not delaying because they lack intelligence. They are delaying because the path to value still feels uncertain. That matters because hesitation has a time cost. Every week spent waiting to try, overthinking the right use case, or worrying about doing it wrong is another week of lost learning, lost efficiency, and lost momentum. If we want confident AI adoption, we need to understand that delay is often less about ability and more about friction. ------------- Delay is often a protective instinct ------------- When people hold back from using AI, it is easy to label them as resistant. But in many cases, they are trying to protect their time, reputation, and standards. They do not want to invest energy into a tool that feels unclear. They do not want to produce something low quality. They do not want to depend on a system they do not fully trust. That caution is understandable. In most professional settings, people are rewarded for being reliable, not experimental. So when a new tool appears, especially one surrounded by hype, many thoughtful people slow down rather than rush in. The problem is that this protective instinct can quietly become expensive. The effort to avoid wasting time often turns into a larger form of time loss. Instead of running a few small experiments and learning quickly, people stay stuck in observation mode. They keep reading, watching, comparing, and waiting for certainty that rarely arrives first. That creates a frustrating pattern. The longer someone waits, the more unfamiliar the tool feels. And the more unfamiliar it feels, the more energy it seems like it will take to begin. Delay then reinforces itself. ------------- Smart people often want to use AI correctly before they use it at all ------------- This is one of the biggest hidden barriers. Many high-performing people do not like feeling inefficient at the start. They are used to competence. They are used to being the person who knows how to approach a task well. So when AI introduces a learning curve, even a small one, it creates discomfort.