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Tonya Surman's avatar

Well there you go… and I thought that the answer was a circle… I stand corrected : )

Eamon Montgomery's avatar

Indy, I'm a special education teacher in Wisconsin. I've been building a recognize-decide-act framework for two years that I think touches the same thing you're working through here, from a completely different direction. Something about your piece, and many of your pieces, strikes me as overlap.

I can feel the geometry of your helix-versus-orbit distinction. The idea that revisitation only matters if it compounds — that coming back to the same problem in the same condition is repetition, not progress. So what determines the outcome?

Whether recovery actually completed before the next cycle started.

I've been working with a dimensionless ratio — same mathematical shape as a Reynolds number — that measures whether a system's internal recovery speed can keep pace with the forcing it's under. Recently I extended it to account for exactly what you're describing: what happens when a system revisits a challenge from an incomplete state.

The Amazon rainforest was hit by severe drought in 2005. Recovery rates varied across the vast affected area — some portions of the forest hadn't returned to baseline five years later when the 2010 drought arrived. The second event was dramatically more destructive, and the areas that had recovered least were hit hardest. Not because the drought was worse. Because the forest that met it was still depleted. That's your orbit. Same rhythm, degrading capacity.

Bone does the same thing. Load it with rest and it remodels stronger — your helix, compounding capability. Load it before it heals and it fractures. Same input, opposite outcome. The variable is recovery completeness. I can calculate that now across biological, ecological, and organizational systems, and the threshold behaves the same way in all of them.

Your curvature idea maps onto something I've been calling pivot — the capacity of a system to change direction before it's forced to break. I've been building what I call institutional stabilization tripwires: built-in pivot points that fire before irreversible damage locks in. The tripwire fires, the system pivots. What you're calling curvature is the capacity to pivot — how much room the system still has to turn. I think there might be a ratio there too: how fast a system can adapt divided by how fast its commitments are hardening around it. A pivot ratio. When it's healthy, the system can turn. When it degrades — when you're hardening faster than you're learning — you've lost the pivot. The question stops being "are we going the right direction" and becomes "can we still turn at all?" Those are fundamentally different questions, once you pass a point of no return, with fundamentally different design implications and outcomes.

The thing I'd add — and this is what I've been working on most recently — is that harm moves through systems at different speeds depending on what level you're looking at. Market pressure on Boeing was slow and diffuse at the whole-system level. It is transmitted to organizational processes as repeated cost-cutting cycles at the relationship level. It manifested as a specific software failure at the component level. Slow became medium became fast. Your helix operates differently at each of those levels simultaneously, and I think pivot capacity needs to be tracked at each level separately. A system can look like it's turning fine at one level while it's locked rigid at another.

The thing that makes this urgent is that we're watching these dynamics play out in real time. I write a Substack where I've been tracking what I call the Hidden Recession — the way economic and institutional stress compounds invisibly until the snapshot metrics can't explain what people are actually experiencing. I've also been tracking specific institutional trajectories — OpenAI's governance crisis, Florida's political-economic feedback loops in 2026 — and the pattern is exactly what your piece diagnoses.

The snapshot says the system is fine. The trajectory says it's degrading. The reason the snapshot lies is the thing you named: the system is revisiting its problems from an incomplete state. Each pass looks like the same cycle. It's actually a lower baseline. We're measuring the position and missing the direction. We're measuring the direction and missing the depletion.

Your curvature question and my pivot question are the same question from different directions: can these systems still turn? Not just — are they going the wrong direction. Can they turn at all. And if not — if the commitments have hardened faster than the learning — then we're not watching governance or management anymore. We're watching physics. Better to be on the side of physics.

I'd be glad to share the framework if you're ever curious. But mostly I wanted you to know that a special education teacher working away with his AI on his off hours arrived at the same structural diagnosis you did, from a completely different starting point. That's convergence.

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