Why Systems Break When They Grow Too Fast
Most systems don’t fail because they run out of resources. They fail because they change faster than they can understand what they’ve become. You can see this once you start paying attention, though it usually isn’t taught as a single thing. A team grows and suddenly meetings feel strange. An AI model behaves in ways no one quite remembers training. A person steps into a role that technically fits, but something about it doesn’t hold.
People reach for familiar explanations—burnout, misalignment, scaling issues, culture, bad incentives. Those labels describe the surface. They don’t explain the mechanism. What’s actually happening is quieter. The system’s sense of itself is expanding faster than its ability to reorganize around that expansion. When that gap opens, coherence starts to slip. Not immediately. Not dramatically. First, things just feel harder to reason about.
Identity Moves Faster Than Adaptation
Any system that persists—human, organizational, computational—has at least two internal functions, whether or not it names them. One is identity: some internal sense of what the system is, what counts as “normal,” what actions make sense. The other is adaptation: the machinery that updates that identity when reality changes.
When those two move together, growth feels clean. The system learns. It becomes something new without losing continuity. But when identity expands faster than adaptation can reorganize, the system doesn’t smoothly evolve. It destabilizes. Information stops lining up. Decisions feel inconsistent. Small issues begin to echo. People start saying, “This used to be obvious.” Nothing is “wrong” yet—but something is no longer holding.
I didn’t start thinking about this because of theory. I started noticing it because the same pattern kept showing up in places that had no business resembling one another.
Intelligence Is Not Just Processing
Modern AI makes this easier to see, because the changes happen quickly and without narrative cover. We scale models. We increase data. We add parameters. And then, sometimes abruptly, behavior shifts. Capabilities appear that no one explicitly programmed. Models begin to act less like tools and more like agents. Researchers call this “emergence.”
That word gestures at mystery, but the underlying behavior isn’t mysterious. The system’s internal identity changed faster than its adaptive machinery could reorganize. So it slipped into a new attractor—a different way of holding itself together. Humans do this too. So do organizations. The substrate changes, but the architecture doesn’t. Growth is easy. Remaining coherent while growing is not.
Where Things Actually Break
By the time a system is visibly failing, the break already happened earlier—at the level of internal self-modeling. That’s why interventions aimed at symptoms feel frustratingly indirect. You can optimize processes, add controls, change incentives, or coach behavior, and still feel like you’re pushing against fog.
The system isn’t resisting you. It’s trying to become something it doesn’t yet know how to support. Once you see that, a different set of questions becomes available. Where is identity expanding faster than adaptation? What assumptions are no longer being updated? What internal models are being stretched beyond their reorganization capacity? Those questions tend to matter more than the usual ones.
What I Pay Attention To
My work sits in this gap—between growth and coherence. I pay attention to the moment when a system is still functional but no longer internally aligned with itself. When it hasn’t collapsed, but it’s starting to feel brittle. When people sense something is off but can’t yet say what.
Humans encounter this individually. Teams encounter it collectively. Intelligent systems encounter it structurally. The same failure mode shows up under different names. If this pattern feels familiar, you’ve probably already been living inside it.
A more formal, technical treatment of the structure behind this pattern exists for readers who want to go deeper.