For a while, our trust conversation around AI has centered on the model. Better accuracy, fewer hallucinations, cleaner citations. Our assumption has been that as the technology improves, trust will follow automatically. But new survey data tells a different story. The overwhelming majority of workers, ninety seven percent, still default to their own judgment or a colleague's before they turn to AI output. Seventy two percent said they would side with a coworker over AI if the two gave conflicting answers. That is not a technology gap closing slowly. It is a structural pattern that looks stable even as the tools keep improving. That matters because it points to something we keep underestimating. Trust was never going to be a feature added to a model. It runs through us, and protecting that layer is becoming one of the more valuable things we can do as teams. ------------- Context ------------- Most of us right now are focused on getting more AI output moving faster. Drafts, summaries, analyses, first passes at almost everything. That push makes sense. Output speed is the easiest thing to measure and celebrate. But speed of output was never the same thing as trust in that output. A draft that arrives in ten seconds instead of ten minutes still has to be checked, corrected where it is wrong, and vouched for by someone before it moves forward. That checking step does not disappear as AI gets better. It just becomes less visible. This is where human judgment starts to look less like a temporary workaround and more like permanent infrastructure. Not a stopgap until the model gets good enough, but the actual mechanism that makes any AI output usable in a real decision. The mechanism is straightforward. A person applies context the model does not have, weighs a tradeoff the model was never asked to weigh, and puts their name behind the result. That act of vouching is what turns raw output into something we can act on. For a Stay Human pillar, that is the whole point. AI can accelerate our draft. It cannot accelerate our trust. That still has to be built by a person, one decision at a time, and protecting the space for that is a human outcome worth designing around, not a delay to engineer away.

Many of us assumed that being serious about AI meant running a growing stack of tools alongside the work. One for writing, one for research, one for meeting notes, one for scheduling, one for images. The more of them we had installed, the more capable we felt. But the pattern showing up across AI adoption research tells a different story. People using a small handful of AI tools are reporting better results than people juggling a large collection of them. Somewhere around three tools, the curve bends. Add a fourth, a fifth, a sixth, and the reported gains start dropping instead of climbing. That is a simplicity problem before it is anything else. Every extra tool is another interface to remember, another login, another place to re-explain what we are trying to do. AI was supposed to remove friction, not hand us a longer list of apps competing for our attention. We keep treating this as a willpower issue, as though the fix is simply trying harder to keep every tool current. It is not. It is a design choice, and it is one we can reverse starting with the very next tool we are tempted to add. ------------- Context ------------- Most of us are collecting AI tools the way we once collected browser extensions or productivity apps. A new one launches, it looks useful for one specific job, we add it, and it sits alongside everything we were already using. The trap is treating each new tool as pure addition. In practice, a growing stack does not stack neatly. Recent research on AI use at work found that a large share of people juggling multiple AI tools describe genuine tool sprawl, and that number climbs higher for people whose employer requires AI use rather than lets them choose it. Roughly a third have three or more tools that all do close to the same thing. This is where subtraction becomes the more useful move than addition. Instead of asking what new tool could help with the next task, the sharper question is which of the tools we already have could handle it, and which of the ones we barely open could be removed entirely.

Most of what we hear about AI revolves around business. Automate this. Write that. Build an agent. Save time. Make more money. I use AI for all of those things, but one of the areas where I've found just as much value is personal growth. I've started looking at AI as more than an execution tool. It can also be a thinking partner. I can take an idea I'm struggling with and work through it from several perspectives. I can challenge my own assumptions instead of automatically defending them. I can take something that didn't go well and ask: What could I have done differently? I can even take principles I'm trying to live by and turn them into practical actions instead of leaving them as ideas in my head. And there's an interesting parallel to what we've been learning while building AI agents. Garbage instructions create garbage execution. The same thing happens personally. If I haven't clearly defined what I'm trying to accomplish, what my priorities are, or what I'm unwilling to compromise, it's pretty hard to make consistently good decisions. AI can help expose those gaps. It can ask the uncomfortable follow-up question. It can show you contradictions between what you say you want and what your actions are actually producing. It can help turn a big goal into tomorrow morning's first action. But there's an important boundary: AI shouldn't replace your judgment. It should help sharpen it. The goal isn't to outsource thinking, responsibility, relationships, or personal decisions to a machine. It's to use the machine to become more deliberate about them. That's probably one of the biggest changes AI has created for me. I'm using it to help build companies, systems, and opportunities, but I'm also learning to use the same technology to examine and improve the person responsible for building all of them. Business growth compounds when the person behind the business grows too. How are you using AI for something other than work or business?

I'm in Cologne, Germany right now standing across from something that stopped me in my tracks. A cathedral that started construction in the 1200s. Took 650 years to finish. No cranes, no computers, no technology anywhere close to what we'd consider basic today. Just someone with a dream so big that none of the resources existed yet to make it real. And they built it anyway. That's been hitting me hard on this trip. Because everywhere I've gone, whether it was the Roman roads in southern Italy, the 3,000 year old olive trees, or the sewer systems built before most of us can even imagine, the same thing keeps showing up. The people who created what we now stand in front of in awe weren't waiting for the right tools or the right time. They were just resourceful enough to keep going despite every reason not to. And I keep thinking about how many of us have something like that inside us. A business, a dream, an idea we've been sitting on. And the voice that says it's too hard, too technical, too late, or too big of a reach. That voice has been wrong for a very long time. The evidence is literally carved in stone all over the world. What's one thing you've been telling yourself is too big to actually pull off? 🏛️

Most of us calculate AI's value the same way. We look at the task it used to take an hour and now takes ten minutes, and we call the other fifty minutes saved. That math feels obvious. It is also incomplete. What we are actually seeing, once teams track a full week instead of a single task, is a second column showing up next to the time saved. Recent workplace data puts it at something like six hours a week spent checking AI output, fixing what it got wrong, and rerunning prompts that did not land the first time. People are starting to call this botsitting, and once you have a name for it, you start noticing how much of the week it quietly eats. That matters for time because the win we are counting at the point of creation is not the win we are keeping. A chunk of it gets handed straight back to review, correction, and re-generation, and if we are only measuring the first ten minutes, we are missing where the rest of the hour actually went. ------------- Context ------------- Right now, most teams still budget AI the way we budget a faster tool. We assume the task shrinks and the freed-up time is ours to redirect toward something better. That assumption drives a lot of the adoption enthusiasm we see across the community: faster drafts, faster summaries, faster first passes. But a faster first pass is not the same as a finished one. AI output that looks complete can still be wrong in ways that are easy to miss on a skim and expensive to miss on delivery. Wrong tone, wrong numbers, a missed nuance in a client email, a generated summary that quietly drops the one caveat that mattered. None of that shows up as an error message. It shows up later, usually after someone has already trusted it. This is where botsitting becomes a useful frame rather than just a complaint. It names the specific work of supervising a fast but unreliable collaborator: check the output, catch what is off, decide whether to fix it or start over. That is a real task with a real time cost, and it deserves its own line in how we plan our week, not a footnote.