Reading time: 5 min
A few busy weeks behind me. Lots of open threads, changes to make, decisions in my hands, and decisions out of my control to wait on.
That's what inspired this edition. We're talking about open loops, or the fancier name: the Zeigarnik effect. And we're closing this newsletter batch.
A trip to Vienna, 1927
A Soviet psychologist sat in a Vienna café and watched the waiters work.
They were holding a dozen complex orders in their heads at once. Table 4 wanted goulash. Table 7 wanted strudel and coffee. Table 11 was still deciding. No notes, and also no mistakes.
But the moment a table paid and left, the order vanished from the waiter's memory. Ask them five minutes later what table 7 had eaten, and they'd be blank.

AI generated
Bluma Zeigarnik made these observations. They later became one of the most popular effects in psychology: the brain holds mental space for unfinished tasks and lets go of finished ones.
This is the Zeigarnik effect, or simpler said: open loops are like loud music for the brain, closed loops are the calm sounds that eventually go quiet.
Open loops compete for attention.
Closed loops release it.
It's why an unsent email stays in the back of the mind all day. And why a decision we're waiting on (from someone else) drains energy and creates mental fatigue.
WHY this matters
Zeigarnik's findings were just an observed pattern. And decades later, researchers Masicampo and Baumeister* found something even more useful: we don't need to finish a task to free mental space. We need to create a concrete plan for it. If the brain trusts that the loop has a planned solution, it stops sending reminders.
*Check their paper: Consider it done! Plan making can eliminate the cognitive effects of unfulfilled goals
We don't have to do everything. We just have to decide what happens to each open loop (at least with those we have control over).
HOW to close the loops
I wrote about the brain dump before (in 2.3 The #1 Skill of the Future). This time we go one level deeper, not just the brain dump, but what to do with each item.
Step 1: Brain dump
Step 2: Label every item with:
DELETE. Something not happening or that doesn't matter anymore. Just delete it and let the brain gain some space.
PARK. For revisiting, but not now. Pick a date for the revisit, though. This is the trick. Without a date, "park" becomes another silent open loop.
DO. It needs action. But add a deadline so the brain can find some comfort in the unknown.

AI generated
A few highlights from this batch (#2.1 → #2.10)
This whole batch’s highlight was about the skills of the AI age: "becoming high-agency, generating ideas, being resilient, being adaptable" and diving into the HOW.

AI generated
The frogs in the milk
Two frogs fall in a bucket. One looks around, sees no way out and gives up. The other doesn't know how to get out either, but keeps kicking all night until it churns the milk into butter and climbs out.
High agency is when you find a way. Low agency is when you find an excuse.
The Post-it accident
A "failed" adhesive, too weak to bond properly, sat in a drawer at 3M for years. Then someone needed a bookmark that wouldn't damage paper, and the failure had a name.
Ideas show up when the mind is fed and given room to wander.
Pain is not the same as suffering
Edison's thousand trials, Rowling's rejections before Harry Potter, the stories we all know about and use as an inspiration to keep going.
Resilience is recovery speed, not the absence of falling.
Potato, egg, coffee bean
Same boiling water, but three different outcomes. What changes one isn't what happens, it's what the person decides to do about the situation.
Adaptable individuals change their circumstances by not letting external events influence their state.
And running through every one of these self-growth subjects was AI. The topic we all talk about, but never seem to know enough about.
Just to raise some questions, here are 2 videos that caught my attention this week, looking at AI changes (and costs) from different angles:
Closing with a question
This brings me to a question I want to close this batch with:
Do we still learn and grow, or just use AI to solve the never-ending loop of optimization?
The default leans heavily toward optimization. We all hear: automate, save time, remove friction. Which is good. But if all the saved time goes back into more of the same, we just get faster. Are we any wiser?
I don't have a clean answer, so I’ll keep digging until I find the right resources.
If you got here, thanks for reading!
Until the next one, wishing you smiles and sunshine for the week ahead,
Silvia

