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⚡ The Fastest Teams Are Not Using AI for Everything, They Are Using It at the Right Moments
One of the easiest mistakes teams make with AI is assuming more usage automatically means more value. Once people start seeing time savings, the temptation is to apply AI everywhere, to every task, every workflow, every stage of work. But the fastest teams usually do something more disciplined than that. They do not use AI for everything. They use it at the moments where work tends to slow down, stall, or loop back. That distinction matters because not every task creates the same kind of drag. Some tasks move fine without intervention. Others create delays, rework, handoff confusion, or blank-page friction that quietly stretches cycle time. The teams getting the best results are usually the ones that know where those slow points are and apply AI there first. ------------- More AI usage is not the same as better AI usage ------------- It is easy to think adoption success should be measured by how often AI appears in the workflow. But high usage on its own can be misleading. A team can use AI constantly and still save very little meaningful time if it is being applied in the wrong places. This happens when people focus on novelty instead of friction. They try AI on random tasks, experiment broadly, and generate a lot of activity without identifying where the real delays are. The tool becomes present, but not necessarily useful in a way that changes the pace of work. The better question is not, “Where can we use AI?” It is, “Where does work keep slowing down?” That is where time savings tend to become visible and repeatable. Maybe it is the first draft that always takes too long to start. Maybe it is the handoff where details get lost. Maybe it is the review stage where messy inputs create extra rounds of correction. These are not glamorous problems, but they are often expensive ones. Fast teams understand that the point is not broad insertion. The point is targeted friction removal. ------------- The biggest gains usually live at the stall points -------------
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⚡ The Fastest Teams Are Not Using AI for Everything, They Are Using It at the Right Moments
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Anthropic Made a Safer OpenClaw & More AI News You Can Use
In this video, I show off Claude Dispatch, which is essentially a clone of OpenClaw but safer and easier to use. Anthropic wasn't alone though, as Perplexity and Manus both released OpenClaw copies (OpenClawpies?) themselves. I also break down this trend in AI along with the new Gemini-based updates to Google Workspace and Google Maps, and more. Enjoy!
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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?
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?
HIPAA compliant AI tools
I am a solopreneur insurance agent in the Medicare space - fancy way to say I sell supplement plans and Advantage plans. I would like to use AI in my business but need to stay HIPAA compliant. My CRM and even my email can have Protected Health Information contained within making skills inside standard plans like Claude and Copilot unsuitable. I conduct drug reviews, discuss health conditions and have personal identifiers. Does anyone know of an affordable platform that will allow me to automate some of my business that is HIPAA compliant for this industry? Business and Enterprise plans are compliant but they are thousands of dollars. Any ideas?
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