We often talk about trust in AI as if it is an emotion we either have or do not have. But trust does not scale through feelings. Trust scales through systems, the visible structures that tell us what happened, why it happened, and what we can do when something goes wrong. ------------- Context: Why “Just Be More Careful” Is Failing ------------- As synthetic content becomes more common, many people respond with a familiar instruction: be more careful, double-check, trust your gut. That advice sounds reasonable, but it quietly shifts the entire burden of trust onto individuals. In practice, individuals are already overloaded. We are navigating faster communication, more channels, more content, and more urgent expectations. Adding constant verification as a personal responsibility does not create safety. It creates fatigue, suspicion, and inconsistent outcomes. The deeper issue is that the internet and our workplaces were built for a world where content carried implicit signals of authenticity. A photo implied a camera. A recording implied a person speaking. A screenshot implied a real interface. We are now in a world where those signals can be manufactured cheaply and convincingly. So the question becomes less about whether people can detect fakes, and more about whether our systems can support trust in the first place. When trust is treated as a personal talent, it becomes fragile. When trust is treated as an operational design problem, it becomes durable. ------------- Insight 1: Detection Is a Game We Cannot Win at Scale ------------- It is tempting to make trust a contest. Spot the fake. Find the glitch. Notice the strange shadow. Compare the audio cadence. This mindset feels empowering because it suggests that skill equals safety. But detection is inherently reactive. It assumes the content is already in circulation and now we need to catch what is wrong with it. As generation quality improves, the tells become fewer, subtler, and more context-dependent. Even if some people become excellent at detection, the average person will not have the time, tools, or attention to keep up.

Hi all, Wanted to share something I’ve been playing with in the hopes it can help you too. I originally shared a post about how the senior executives I work with are using AI to refine their LinkedIn profiles and attract their next role. (my specific niche) I’ve also shared this with my entrepreneur friends, and it works just as well for us as founders looking to sharpen our positioning. “The riches are in the niches,” everyone keeps telling us. And this is something I struggled with for a long time. Getting clear.Getting specific.Owning it. So here’s something you can experiment with. Take your current LinkedIn About section. Drop it into AI. Then try these prompts. 1️⃣Review what you know about me and ask me questions until you are 95% clear on who I serve and the outcomes I help them achieve. Then rewrite my LinkedIn About section in the style of a successful marketing copywriter you resonate with. (I chose Dan Kennedy) 2️⃣What would the top 1% of the clients I want to attract think when they land on my profile? 3️⃣What are the top 5 red flags potential clients might see when reviewing my profile or content? 4️⃣For each red flag identified, suggest specific changes to strengthen my positioning and reduce friction. 5️⃣Based on my profile and target audience, suggest stronger headline options that clearly communicate who I help and the transformation I deliver. And one more I’ve been testing: 6️⃣How can I refine my message so it feels distinct, memorable, unique and impossible to confuse with anyone else in my space? Don’t overthink it. Play with it. See what comes back. You don’t have to agree with everything it says. But sometimes it holds up a mirror you didn’t know you needed. Hope it helps. Happy experimenting. Would genuinely love to hear what shifts for you shout out and thank you to @Sabrina Ramonov from the AI advantage prompts for the initial inspiration and prompts that got me started down this rabbit hole.