When there are multiple ways to accomplish something with AI, one faster and simpler, another slower but involving more genuine engagement, the faster option almost always wins by default. This makes intuitive sense: the whole point of adopting AI is speed and efficiency, so choosing the fastest available path on any given task feels like a straightforward application of that goal. But there's a cost to always defaulting to the fastest path that only becomes visible over a longer time horizon: the slightly slower approaches often produce learning, durable systems, or quality improvements that the fastest path skips entirely. Optimizing every single task purely for immediate speed can quietly cap how much better someone's overall AI-assisted work gets over time, even as each individual task gets handled efficiently. ------------- Context ------------- The tension here is between two different kinds of time value: the time saved on this specific task right now, and the compounding value that a slightly slower, more deliberate approach might build for every future task of a similar kind. These two values pull in different directions, and defaulting reflexively to the fastest path optimizes entirely for the first at the expense of the second. A simple example illustrates the pattern clearly. Faced with a recurring task, someone can either ask AI to just produce the output directly, which is the fastest path, or they can take a bit more time to understand why a particular approach works well, to build a reusable template or framework from the interaction, or to develop a clearer sense of what good output looks like for that task category. The first path is faster in the moment. The second path takes somewhat longer now but produces a durable asset, whether that's a template, a sharpened judgment, or a piece of genuine skill, that makes every future instance of that task faster and better than the first path alone would have produced. Across many repetitions of a task, the compounding value of the slightly slower path can dramatically exceed the value of the fastest path repeated the same number of times, even though each individual instance of the fastest path was, in isolation, more time-efficient.

In MI we encountered an extreme heat wave followed by a serious smoke event. Required me assess the situation, act and adjust accordingly. Gemini and I figured it out. I threw out ideas on the and Gemini helped with detail, and also cheered me on as I put together multiple plans. First set up cooling home with AC and fans,had to deal with extreme moisture. Then the smoke wall came, stagnant and menacing. Conditions were perfect for the smoke to swoop thru my basement and attached garage. So quick I had to scramble to seal off parts of my house to contain it. Finally was able to contain and lessen the smoke and cool my hot home. I was glad to have AI at my side. It was a challengewhere I had to think fast and act fast and problem solve on the fly. I told friends about AL'S help to strategies and problem solve. Learned a lot about heat,smoke and its behavior in extreme conditions. Have many friends who are leery of Ai. Great experience and triumph. I'll be ready for the next one. Thanks Gemini. Sorry so long. To you all: Be great!

Would you rather have: 💰 An extra $100,000 to invest in your business today... OR 🧠 10 extra years of knowledge and experience, but no additional money. You can only choose one. Which are you picking, and why? There isn't a right or wrong answer here. I'm curious to see how everyone thinks. Drop your choice in the comments and tell us what drove your decision. Let's see where the conversation goes.

I'm just curious, do you still say please and thank you when communicating with AI tools?

Hoping this evening brings comfort, reflection, and a sense of togetherness.