I could not have come this far with TM1 without AI. I say that plainly because it is true, and because it is the best example I have of something I could not see until I went looking for it.
I do not have the years of hands-on platform experience that most TM1 developers carry. What I have is decades of process experience: knowing what a business is trying to do, how the pieces connect, and what a good answer looks like. What AI gave me was a well-read assistant for everything else. The result is that I now build at a pace that lets me work alongside developers with ten and twenty years in the platform.
Ask me on any given day whether that feels remarkable, and the honest answer is no. It just feels like work.
What do I mean by a clock?
This is the third post in a short series, after What Does Gravity Have to Do With AI? and Whose Clock Counts?. You do not need them to follow this one.
In this series, a clock is not the one on your wall. It is your internal sense of how long a piece of work should take, and how much effort counts as normal. Everyone carries one. It is how you know a task is late, or fast, or reasonable, without looking anything up.
Now the physics, because it explains why that answer, “it just feels like work,” is exactly what you should expect. The closer a clock sits to a massive object, the slower it runs compared to a clock far away. That is measured, not theoretical. But from the inside, nothing feels different. A clock deep in a gravitational well has no way to know it is running at a different rate. Every second feels like a second. The offset only shows up when you set that clock beside a second one somewhere else.
GPS works this way too. The satellite clock cannot tell you it is off, and neither can the ground clock. The system has to hold both readings at once and keep checking them against each other.
Back to my own clock
My sense of how long things take has been recalibrating the whole time. What used to be a slog is now simply how long the work takes, so it does not register as speed. It registers as Tuesday.
The only reason I can see the distance at all is that I happen to have an unusually good second clock: the veterans. I know how fast they work, and I know how I used to work beside them. Set the two readings next to each other and the offset is obvious. Without that comparison, I would give the answer nearly everyone gives: it just feels like work.
That does not make their clock the true one. Their pace comes from years of judgment I do not have, and it would be a mistake to read my pace as equal to their experience. My pace moved. Their depth is still theirs. What has changed alongside the pace is how I think. The modeling concepts are settling into my own thought patterns, which is not something a tool can do for me. Neither reading is the real one, and the difference between them is what tells me something.
Why data work hides it so well
A finished model looks the same however it was built. A cube, a set of rules, a report does not show whether it took a month or a day, so nothing about the result tells you how far the process moved. The only way a team notices its pace has changed is by comparing against something outside its own sense of normal.
The closer a team gets to the capability, the more ordinary everything feels, and the less likely anyone is to go looking for a second clock.
Reading your position
If you cannot read your own clock from inside, the answer is not to trust the outside view over the inside one. It is to hold both. Ask someone who is not in the well to describe what they see. Compare today’s work against a record of the same work before, not against how it feels. Treat “it just feels like work” as information about where you are standing, not a measurement of how far you have come.
Nobody can read their own clock from inside the well. The useful move is not to find the person who can. It is to put two clocks in the same room and let the difference show.
This is part of a series exploring how AI and conversational interfaces are reshaping data architecture and business intelligence. Previous posts are available at insightsindata.com.
© 2026 Paul Nevill / Insights in Data
