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⏳ The Real Promise of AI Is Not More Output, It Is More Margin
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 -------------
⏳ The Real Promise of AI Is Not More Output, It Is More Margin
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Hard truth…
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?
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Which Top AI Should You Choose & More AI News You Can Use
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 AI is lying to you. It just sounds really good doing it.
I ran an experiment that changed how I use AI forever. I took the SAME prompt and sent it to ChatGPT, Claude, and Gemini at the same time. Not to see which one was "best." I wanted to see where they DISAGREED. Here's what blew my mind: → ChatGPT gave me a confident, detailed plan. Sounded great. → Claude flagged two risks that ChatGPT completely ignored. → Gemini agreed with ChatGPT's plan... but used completely different reasoning to get there. So who was right? They all were. And they were all wrong. Each one had blind spots that the others caught. That's when it hit me — asking ONE model is like hiring ONE consultant and hoping they don't have blind spots. They always do. So I started doing this with every important decision. Three models. Compare the disagreements. The answer is always in the friction between them. A few things I've noticed after months of doing this: → When all three agree, you can trust the answer. When they don't, that's where the gold is. → ChatGPT is the most confident. Claude is the most cautious. Gemini is the fastest to spot patterns in large data. None of them will tell you they're wrong. → The biggest risk in AI isn't a wrong answer. It's a wrong answer that SOUNDS right and you have no way to know. Curious — is anyone else cross-checking between models, or am I the only one doing this the hard way?
The importance of foundational skills.
When using AI, it's really important that we get good at prompt engineering, which is a fancy word for “giving instructions in a clear way”. The big issues I see with this are two things. One, we're actually pretty terrible at communicating. And two, we don't actually know what we want the agent to do. The first one is self-explanatory. We use big, vague words that don't clearly state what we want or what makes an output good versus bad. I'm guilty of this all the time, but honestly, it's a lot of work to write a detailed prompt (especially when all you want is to find the cheapest place to buy groceries). The second one is more important though. I've caught myself wanting AI to write good copy, but then I realize I've never actually defined what "good copy" means. I don't have enough experience to even understand the nuances of what makes a strong foundation for a good piece versus a bad one. I'm using copywriting as the example here because marketing and lead gen is where I'm struggling in business right now, but this applies to anything. If you're having trouble with writing and you try to get AI to write something, of course it's not going to understand what makes it sound human versus not human. You haven't taken the time to explain it. Same thing with AI-generated images and video. We know it can produce good results, but it also produces bad results a lot of the time because the defaults are things like waxy skin and unnatural body compositions. The issue is that we haven't told it the specifics (things like "use a light skin tone with some blemishes and natural imperfections"). These models are exceptional at what they can do. But a lot of the time when we don't get the output we want, it's user error, not a model problem. TLDR: Bad AI outputs is a skill issue.
The importance of foundational skills.
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