User
Write something
Pinned
🚪 AI Adoption Gets Easier When We Stop Treating It Like a Talent Test
A lot of people say they want teams to adopt AI faster, but many of the social signals around AI make adoption harder. The tool gets framed like a test of who is innovative, who is behind, who “gets it,” and who does not. Once that happens, people stop approaching AI as a workflow tool and start experiencing it as a referendum on their ability. That shift creates delay. It adds pressure where curiosity should be. It turns simple experimentation into a performance moment. And it makes the learning curve feel more personal than practical. If we want AI adoption to move faster and create real time savings, we need to stop treating it like a talent test and start treating it like what it actually is, a way to reduce friction in the work. ------------- Performance pressure slows practical learning ------------- When a new tool enters the workplace, people do not respond only to the tool itself. They also respond to the culture around it. If the unspoken message is that capable people should already know how to use AI well, then anyone who feels uncertain is likely to hide that uncertainty instead of working through it. That is where time starts to get lost. Instead of asking basic questions, people stay quiet. Instead of testing a small use case, they wait until they feel more confident. Instead of learning in public through normal trial and error, they try to avoid looking inexperienced. This is a common pattern in high-performing environments. People are comfortable being competent, not visibly early. So when AI becomes tied to status, speed of adoption often slows down. The people who most want to avoid wasting time end up spending even more time observing, second-guessing, and delaying the first useful experiments. The irony is that AI does not usually become valuable through image management. It becomes valuable through repeated practical use. And practical use gets harder whenever people feel like they are being evaluated instead of learning. ------------- AI is not proving who is smart, it is revealing where work is inefficient -------------
🚪 AI Adoption Gets Easier When We Stop Treating It Like a Talent Test
Pinned
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?
Pinned
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!
Checking in...
We are right in the middle of the week and I just want to check in. How are you doing? Not the "I'm fine" answer. The real one. Because whatever you are carrying right now, whatever feels heavy or unfinished or just plain hard, I want you to know that you are closer than you think. Keep going. Wednesday is proof that you already made it halfway and that matters more than you know. 🥰
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?
1-30 of 12,042
The AI Advantage
skool.com/the-ai-advantage
Founded by Tony Robbins, Dean Graziosi & Igor Pogany - AI Advantage is your go-to hub to simplify AI and confidently unlock real & repeatable results
Leaderboard (30-day)
Powered by