In his seminal essay “No Silver Bullet,” Brooks defined the concept of accidental versus essential complexity in software development. Essential complexity is the irreducible complexity of the problem you want to solve. Accidental complexity is the complexity introduced by implementation choices, which we aim to minimize.
Typing was never the bottleneck in software development, but managing accidental complexity is. There are good reasons to think AI can effectively address accidental complexity significantly.
However, to understand where AI can make the greatest impact, we must first examine where this accidental complexity originates. A major contributor is that, by default, we engineer systems using technologies designed for internet-scale applications. If your software is internet-scale, the complexity of modern frameworks is partly essential—scaling brings irreducible complexity with it. But if your system isn’t internet-scale, all this complexity is accidental. Instead of a simple PHP file, you now have an Angular application with a separate frontend and backend in a three-tiered architecture consisting of at least 20 files. You are expected to build your application cloud-native, even if you don’t really need the cloud.
There are many straightforward refactorings that teams don’t implement because they are too expensive. Think of changing the name of a domain attribute in your model. It can easily ripple through many artifacts—from domain models to unit tests to database schemas. Basically, many cross-cutting changes are usually not carried out because they have poor economics. Over time, the consequence is design erosion.
AI excels at these changes, executing ripple effects across the codebase without the cost or fatigue that currently deter human developers. AI changes the economics of software maintenance. Consequently, aiming for 100% consistency shifts from an ideal to a practical reality.
Related
- This post has been inspired by https://ian-cooper.writeas.com/is-ai-a-silver-bullet
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