A lot of AI conversations still revolve around one promise, more output. More content, more tasks completed, more ideas generated, more work pushed through the system in less time. That promise is real, but it is also incomplete. Because if all AI does is help us produce more, then many teams will end up faster without feeling any better off. The deeper value of AI is margin. It is the ability to create breathing room inside the workday, reduce unnecessary friction, and give people more space to think, decide, and focus. That matters because time saved only becomes valuable when it changes the quality of work or the quality of life around that work. Otherwise, efficiency just becomes a faster way to stay overwhelmed. ------------- More output is not always the same as more value ------------- For years, most workplaces have treated productivity as a volume equation. If we can do more in the same amount of time, that must be progress. On the surface, that makes sense. But in practice, more output does not automatically create better outcomes. A team can produce more drafts, more meetings notes, more messages, more updates, and still feel buried. In some cases, extra output creates even more to review, more to manage, and more to respond to. The work expands, but the sense of control does not. This is why the output-first mindset can become a trap. It assumes the main problem is that not enough is getting done, when the real problem may be that too much attention is being consumed by low-value effort, repeated work, and constant switching. If AI only accelerates that cycle, it may improve speed without improving the actual work experience. That is where margin becomes a more useful goal. Margin means some of the saved time stays saved. It becomes available for clearer thinking, better prioritization, stronger review, or simply less pressure at the edge of the day. That is a different kind of productivity win. ------------- Time saved has to be protected, or it disappears -------------

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!

Your life usually doesn’t fall apart all at once. It drifts. A little less focus. A little more distraction. A little more scrolling. A little less doing the things you know you should be doing. And over time, that adds up. I’ve learned this the hard way more than once. If you want to build something meaningful, you have to protect your focus like it’s your job. Because in a lot of ways… it is. Not every opportunity deserves your time. Not every opinion deserves your attention. Not every thought deserves to be followed. Stay locked in on what actually matters. That alone will put you ahead of most people. So, what are you focused on right now and what are you going to do this week to protect that focus at all cost?

One of the biggest changes AI is creating is not just faster output. It is faster adaptation. The people and teams gaining the most are often not the ones who know the most at the start. They are the ones who can reduce the time it takes to learn, test, adjust, and become useful in a new way of working. That matters because the future of work is not being shaped by one tool. It is being shaped by constant change. New systems, new workflows, new expectations, new ways to create value. In that environment, one of the most important advantages is not expertise alone. It is the ability to shorten the learning curve so time-to-competence and time-to-value get smaller. ------------- The old advantage was knowing more, the new advantage is learning faster ------------- For a long time, professional advantage came from building stable expertise and applying it repeatedly. That still matters. But the environment around that expertise is changing faster than it used to. Tools evolve. Processes shift. Roles expand. What worked well last year may already be too slow, too manual, or too fragmented now. That creates a new kind of pressure. The question is no longer only whether we can do the work. It is whether we can learn the next way of doing the work before unnecessary time gets lost. Teams that adapt slowly do not just fall behind strategically. They spend longer inside outdated processes, longer inside avoidable friction, and longer inside work that takes more effort than it should. This is why learning speed has become a time issue. A long learning curve means a long delay before value shows up. It means slower onboarding, slower experimentation, slower adoption, and slower returns from the tools already available. AI makes this more visible because it can reduce the effort required to get started. It can explain concepts, structure messy ideas, create examples, generate first drafts, and help people move from confusion to traction faster. The point is not that AI replaces learning. The point is that it can shorten the slowest part of the path.

The team and I did this and some of the results were very interesting! Go into ChatGPT and post this prompt: "Based on all the conversations we've had, can you analyze my behavioural tendencies. Keep it short and concise" Post your response in the comments below and let us know if any of it was a surprise to you! Here is mine: Here are the behavioural tendencies you consistently show in our chats: - Iterative builder: You like shipping a version, then tightening it fast with specific edits and constraints. - Framework-first thinker: You gravitate toward checklists, systems, repeatable steps, and clear structure over vague advice. - High signal standards: You push for longer, fuller answers with real substance, not surface-level answers. - Precision + optimisation bias: Whether it’s macros, pricing, positioning, or scripts, you like measurable targets and tuning variables. - Creative but directed: You enjoy big imaginative swings, but you still want them organized and usable. - Action orientation: You regularly steer toward “what do I do next” and practical outputs you can deploy immediately.