The Trails That Changed How I See Systems
March 14, 2025 · 6 min read
Have you ever noticed those worn trails cutting across perfectly manicured lawns? The ones that ignore the carefully planned sidewalks, creating their own routes between buildings?
I always noticed them. Something about those unofficial paths felt significant, but I couldn't quite put my finger on why. Their ubiquity seemed to signal something important about human nature or how the world actually works.
Then I learned they had a name: desire paths.
That fact that this phenomenon had a name started to gradually unlock something for me. I wasn't just seeing worn trails in grass. I was seeing a pattern that shows up everywhere humans interact with systems—a pattern that reveals something fundamental about the difference between what we build and what we actually need.
The Discovery
Desire paths emerge wherever people have the freedom to find their own solutions. They're the routes people actually take, not the ones planners intended. They appear in universities, neighborhoods, public spaces—anywhere individuals can vote with their feet about what path is most efficient to them.
But here's what made this concept so powerful for me: once you learn to see desire paths, you start recognizing them in other domains, not just physical spaces.
That spreadsheet everyone uses instead of the official tracking system? Desire path.
The informal slack group that actually get things done while official meetings accomplish nothing? Desire path.
The workarounds people create because the proper process is too slow, too rigid, or too disconnected from real needs? All desire paths.
Seeing the Pattern in the Wild
Professionally, I am an enterprise solutions architect, specializing in enterprise scale GraphQL implementations, governance and systems design– specifically Federated GraphQL at Apollo GraphQL. For the non-technical: I work with large engineering teams across many industries, helping design software systems that are supposed to make work easier, faster, and more reliable.
All too often, the teams I work with have developed their own desire paths around their existing organizations solutions and platforms. More frequently however, Im working with the very platform teams who are trying to stop the emergence of these desire paths altogether.
Over the past three and a half years, I've had a front-row seat to view this pattern playing out in some of the world's largest organizations. Every large-scale software system I've encountered–that has autonomous, or even semi-autonomous teams– has spawned unofficial solutions that work better than the official ones, at least in some regards. To me, these unofficial solutions are desire paths.
Desire paths are signals that highlight the same fundamental disconnect: organizations designing certain systems like machines when they're actually dealing with ecosystems.
The Lens That Changed Everything
This led me to a crucial distinction that shapes how I see everything: the difference between complicated and complex systems.
A Complicated system is a car engine—lots of moving parts, sophisticated engineering, but ultimately predictable. The same inputs produce the same outputs. When something breaks, you can trace the cause and fix it systematically.
A Complex system is a forest ecosystem—full of autonomous agents making independent decisions. Small changes can have unpredictable effects. The same intervention in two similar systems might produce completely different outcomes.
Here's the key insight: the moment you have autonomous agents making independent choices, you're guaranteed complexity.
Organizations, markets, software platforms, even families—if there are people involved making autonomous decisions, you're dealing with a complex system, not just a complicated one.
The Signal Hidden in Plain Sight
This is why desire paths matter so much. They're not just shortcuts or rule-breaking. They're signals. They're telling you that autonomous agents (people, and more frequently AI agents) are finding the current system too rigid, too slow, or too disconnected from their actual needs.
Every desire path is feedback. Every workaround is a vote of no confidence in the official solution. Every shadow system is a prototype of what the official system could become if it served real needs instead of theoretical requirements.
Most importantly, desire paths reveal when we're trying to govern complex systems (full of autonomous agents) with complicated-system tools (rigid processes designed for predictable outcomes).
The friction isn't a bug—it's a feature. It's the system trying to tell you something important about the gap between design and reality.
The Machine vs. Ecosystem Problem
The fundamental mistake I see repeatedly is treating ecosystems like machines. Designing processes as if people were predictable components. Creating governance as if human behavior could be controlled rather than influenced.
When you try to manage an ecosystem with machine-like precision, over time you get what I call organizational senescence—layers of process that prevent adaptation while consuming resources, AKA Red Tape or bureaucratic bloat. People start creating desire paths not because they're lazy or non-compliant, but because the official paths don't serve the work that actually needs to get done.
The desire paths aren't the problem. They're the solution the system is trying to evolve toward.
Why This Lens Matters
Understanding this distinction changes everything. It changes how you approach problems at work, how you think about organizational design, how you interpret the workarounds you see in your own life.
It helps you recognize when you're fighting emergence instead of working with it. When you're optimizing for compliance instead of outcomes. When you're designing for the system you wish you had instead of the one you actually have.
This perspective shapes my thinking—from platform governance ↗ to organizational evolution ↗ to the future of AI systems. Once you see the pattern, you can't unsee it.
Following the Trails
Start paying attention to the desire paths in your own life. Where do you route around official systems? Where do informal solutions work better than formal ones? Where do you see others creating workarounds?
These aren't signs of system failure. They're signs of system evolution in real-time, optimized by the people who actually have to navigate these systems daily.
The trail that everyone's walking but no one officially built? That's the trail that tells you what's actually needed.
The question isn't whether to follow it. The question is whether you'll learn to read what it's telling you about the difference between the world as designed and the world as it actually is.
This lens—seeing desire paths as signals of complex systems being governed with complicated-system tools—shapes how I think about everything from software architecture to organizational design. In my other posts, I explore how these insights apply to specific domains, but it all starts with learning to see the patterns that are everywhere once you know how to look.