The engineers who write the most durable, maintainable code are not necessarily the ones with the deepest technical knowledge. They are the ones who apply a rule that sounds almost embarrassingly simple: before committing any code, they ask themselves whether their grandmother could understand what it does from reading it. Not the syntax, not the logic gates, but the intent. What is this thing supposed to do, and why? Studies on software maintenance costs consistently find that roughly 70% of a software project’s total lifetime cost comes after the initial release, most of it spent on reading and interpreting code someone else wrote. The engineers who internalize that number write code differently from the start.
This connects to a deeper problem in the industry. As explored in “Software Always Takes 10x Longer Than Estimated Because Engineers Are Solving the Wrong Problem From the Start”, the root cause of most engineering delays is not technical difficulty. It is a failure to clearly articulate what problem is actually being solved before a single line is written. The ‘explain it to your grandmother’ rule forces that articulation upfront, before complexity has a chance to obscure the original intent.
Why Clever Code Is Usually a Warning Sign
There is a persistent myth in software engineering that complexity signals competence. A function that compresses six operations into one cryptic line must have been written by someone very smart. In practice, the opposite tends to be true. Research from the University of Cambridge found that code readability is one of the strongest predictors of bug density. The harder a piece of code is to read, the more bugs it tends to hide, not because the programmer was careless, but because obscurity makes errors invisible.
The ‘explain it to your grandmother’ rule attacks this directly. If you cannot describe what a function does in plain language, you do not fully understand it yourself. That is not a sign of intellectual humility. It is diagnostic information. The engineers who apply this rule consistently report that the act of trying to explain code in simple terms regularly surfaces assumptions they had not consciously examined.
This is why code comments are more important than most developers realize. The comment is not a redundant explanation of what the code does mechanically. It is a record of why it exists, what problem it was solving, and what the engineer was thinking at the time. That context is what disappears when someone tries to modify the code eighteen months later, usually under deadline pressure, usually without the original author available.
The Connection to Rubber Duck Debugging
The ‘explain it to your grandmother’ rule shares DNA with a technique that has been independently validated across engineering teams at companies ranging from early-stage startups to firms employing thousands of developers. Rubber duck debugging, the practice of explaining a problem out loud to an inanimate object, works for the same reason this rule works. The act of translating technical logic into plain language forces a mental context switch that exposes gaps in reasoning the author could not see while inside the problem.
Both techniques operate on the same cognitive principle: expertise creates blind spots. The more familiar you are with a system, the more your brain fills in assumed context that a newcomer, or a grandmother, would not have. Writing code that forces you to shed that assumed context is not dumbing things down. It is precision engineering applied to the communication layer of software.
What This Looks Like in Practice
Applying this rule is not about writing less sophisticated code. It is about separating the sophistication of the logic from the accessibility of the explanation. A function can perform a genuinely complex operation and still have a name that describes what it does in human terms, a comment that explains why it was built this way, and a structure that lets a reader follow the decision tree without a guide.
The engineers who do this well tend to follow a few consistent patterns. They name variables after what they represent in the real world, not after their data type or their position in a sequence. They break large functions into smaller ones with names that read like sentences. They write the comment before the code, not after, treating the plain-language description as the specification the code must then satisfy.
This last habit is particularly revealing. Writing the comment first is a form of test-driven development for clarity. If you cannot write a two-sentence plain-language description of what a function should do before you write the function, you are not ready to write the function. Senior developers who build systems that last, the kind that still run reliably years after their authors have moved on, almost universally report some version of this practice. As detailed in “Senior Developers Write Code for Disasters That Haven’t Happened Yet”, the engineers whose work endures are the ones who treat future readers of their code as the primary audience, not the compiler.
The Organizational Multiplier
At the individual level, this rule makes a developer faster and less error-prone. At the organizational level, the compounding effect is substantially larger. A 2022 analysis by McKinsey found that developer productivity varies by a factor of up to four between high and low performers on the same team, and that a significant portion of that gap is explained not by raw coding speed but by the time teammates spend interpreting each other’s work.
Code that explains itself reduces the onboarding cost for new team members. It reduces the investigation time when something breaks at 2 a.m. It reduces the number of meetings required to transfer context from one team to another. All of those are costs that do not show up in a sprint velocity metric but accumulate relentlessly in the background of every engineering organization that tolerates opacity as a standard.
This is especially relevant given how teams are structured today. As companies scale, the cost of communication grows faster than the cost of computation. The engineers who understand this tend to treat clear code not as an aesthetic preference but as a form of organizational infrastructure, as real and as load-bearing as any server architecture.
The Simplicity Paradox
There is a counterintuitive result that emerges from applying the grandmother rule consistently: the code often ends up being technically better, not just more readable. When you force yourself to explain a piece of logic in plain language and find that you cannot, the most common reason is that the logic itself is tangled. The explanation fails not because your prose is weak but because the underlying design has a problem. Simplifying the explanation requires simplifying the code, and simpler code is almost always more reliable, easier to test, and cheaper to modify.
The engineers who internalize this are operating on a different model of what their job actually is. They are not translating requirements into syntax. They are building systems that other humans can understand, maintain, and extend across time. The code is the easy part. The communication is the work.
