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šŸ¤” WE WANT YOUR HONEST OPINION!
We want to better understand what people are TRULY trying to accomplish when it comes to AI so we can make our products better. We know it’s broad and there are so many different lanes, but if you had to pick one of the 2 options below, which one would you choose?
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šŸŽÆ The Skill That Doesn't Show Up on the Task List
Most conversations about AI and productivity focus on task speed: how much faster can a draft, a report, a piece of research get done. That's a reasonable place to focus, since task speed is visible and easy to measure. You can time it. You can compare before and after. The gains are concrete. But task speed isn't where the real leverage is anymore, for a specific and important reason. When AI compresses task time across the board, the bottleneck in most workflows moves somewhere task speed can't reach: the speed at which decisions get made about what to do next. Decision speed, not task speed, is quietly becoming the more important variable, and it's not showing up on anyone's task list because it was never a task to begin with. ------------- Context ------------- Think about what a typical AI-assisted workflow actually looks like now. A draft that used to take two hours takes fifteen minutes. Research that used to take an afternoon takes twenty minutes. The execution layer of most knowledge work has compressed dramatically. What hasn't compressed at the same rate is the layer above execution: deciding what to work on, evaluating whether a direction is right, choosing between options, determining when something is good enough to move forward. This layer was always there. Before AI, it was partially hidden inside the execution time. Deciding what a report should argue happened, in part, while writing it. Deciding which research direction to pursue happened, in part, while doing the research. The thinking and the doing were intertwined, and the total time included both. Now that doing has compressed dramatically, the thinking that used to be embedded in it has to happen more explicitly and more separately. And for a lot of people, that thinking hasn't gotten any faster. It's the same deliberative process it always was, but it's now a larger proportion of the total time a piece of work takes, and it's often the part that isn't being tracked or improved at all. ------------- The Bottleneck Moved, and Most People Haven't Noticed -------------
šŸŽÆ The Skill That Doesn't Show Up on the Task List
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What Success Actually Buys You
Most people think success is about money. It's not. Money is just what buys you options. I've worked hard for decades. Not because I fell in love with the grind, but because I fell in love with what the work could create. Every uncomfortable conversation. Every risk. Every time I wanted to quit but didn't. None of it was just to make more. It was to own my time. To be there for the people I love. To create memories instead of regrets. To have the freedom to say yes to what matters and no to what doesn't. Don't chase success because you want to look successful. Chase it because one day you'll realize time is the only thing you can't earn back. Work hard. Do the uncomfortable things. Become the person capable of creating the life you want. Because real success isn't measured by what you own. It's measured by how fully you get to live. Question for you: If you had complete freedom over your time one year from now, what would you spend more of it doing... and who would you spend it with?
AI replacing people
AI will not replace the man who owns consequence. It will replace the man whose work was only motion.
ā³ Why Being Two Steps Behind in AI Might Be the Smartest Position Right Now
There is significant social pressure in the AI conversation to be current. To know what the newest model can do, to have tried the latest tool, to be adopting the workflow that everyone is talking about this week. Falling behind feels like a risk. Being ahead feels like an advantage. This framing is worth questioning. For a specific type of professional: operators running real businesses with limited time and limited tolerance for expensive mistakes, being two steps behind the frontier is often a better position than being at it. Not because the frontier isn't interesting. Because the cost of being at the frontier is real and often underestimated, and the value of proven, stable approaches compounding over time is real and often underestimated in the opposite direction. ------------- Context ------------- The AI frontier moves fast by design. New models, new capabilities, new tools, new integration possibilities: the rate of change is genuinely high and the announcements are genuinely exciting. For researchers, developers, and people whose professional identity is built around understanding what AI can do, being at the frontier makes sense. The knowledge they develop has direct value. For a solopreneur running a consulting practice, a coach building a client roster, or a small business owner trying to serve customers well, the value of being at the frontier is more ambiguous. The newest capability doesn't always map to a real workflow need. The newest tool often has rough edges that take time and effort to work around. The newest workflow that everyone is talking about may still be in the iteration phase where the failure modes haven't fully emerged. The early adopter premium in AI adoption is real when you're in a position to absorb the cost of being early: the learning curve, the unstable tools, the workflows that need rebuilding when the tool changes significantly, the time spent evaluating things that turn out not to be useful. For operators with limited margin for that kind of overhead, the early adopter premium is often negative.
ā³ Why Being Two Steps Behind in AI Might Be the Smartest Position Right Now
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