Is This Me or the System?
A two-minute tool that gives you a straight, structural answer.
There’s a specific kind of tired that doesn’t show up on any survey.
Not burnout, exactly. Not a bad manager, exactly. It’s the tired that comes from seeing something clearly — a risk, a pattern, a thing that’s obviously not working — and slowly realizing that nowhere in the system is there a place for that observation to actually land. You say it. It gets reorganized into something smaller. Eventually you stop saying it. And from the outside, that looks like things calming down.
I spent close to two decades inside enterprise systems watching this happen, over and over, in companies that genuinely were not trying to suppress anything. That’s the part that took me the longest to understand. Almost nobody is doing this on purpose. The structure just isn’t built to catch it — the incentives, the reporting lines, the quiet rules about what counts as a “real” problem. Truth that doesn’t fit the format doesn’t get rejected. It gets filed under something else. The person carrying it becomes the awkward one. The system reads its own silence as health.
I started calling this the Galileo Problem, because it’s the same shape every time. He wasn’t condemned because his observations were wrong — he was condemned because what he saw threatened the thing the institution needed to stay intact. He recanted to survive. The Church called that resolved. It wasn’t resolved. It was the signal going underground, and it took over 300 years for anyone to formally say so.
Most of us aren’t dealing with anything close to an inquisition, but I’d bet almost everyone reading this has had the smaller version — the meeting where you said something true and watched the room quietly reorganize around you instead of around what you said.
Here’s the thing I eventually landed on, after writing way too many words about it: organizations have never actually named the function that’s supposed to catch this. The person who receives someone’s signal accurately, tells the difference between “this is a them problem” and “this is a system problem,” and carries that distinction upward without flattening it. Nobody requires this. Nobody measures it. It exists by accident, when it exists at all — usually because of who your manager happens to be — and it’s invisible literally by design, because the people doing it well leave no trace. Nothing breaks. That’s the whole point.
I wrote three books trying to trace this all the way down — what it costs organizations (a lot more than anyone wants to admit), what it would actually take to fix structurally, and where the pattern comes from in the first place, which turns out to be a lot older and weirder than corporate America. I’m still deciding what to do with them. But at some point you have to stop describing the thing and build something a person can actually use on a random Tuesday when they’re in the middle of it.
So I built a friction agent.
It’s pretty simple. You describe what’s going on at work, in your own words — the friction, the thing that keeps not landing right, whatever it is. It asks a couple of clarifying questions and gives you back a structural read on what you’re dealing with. Not a verdict on you. A diagnosis of the system around you. Takes about two minutes. Asks for your role, your function, your company, and your country, and that’s it.
And every single one of those, fully anonymous, becomes a data point in something I think is actually going to be useful over time — a real, structural map of how this shows up across companies and industries, instead of just my own pattern-matching from twenty years of watching it happen.
I’m not going to pretend a chatbot fixes a broken org. It can’t extend grace. It can’t make your manager actually listen. It can’t hold anyone accountable for what it surfaces. What it can do is give you a clearer read on what you’re carrying, without you having to do all that interpretive labor yourself while also doing your actual job.
If any of this sounds familiar, try it. Mostly because I think the data matters, but also, selfishly, because I’d really like to know if I’m right.
A book update coming soon — thank you for reading this far.