Embracing the 3-Month Rule: A Practical Approach to Unscalable Solutions

In the world of startups and technology, advice from notable figures often serves as a guiding principle. One piece of wisdom that stands out is Paul Graham’s mantra to “do things that don’t scale.” However, the implementation of this idea, especially in software development, tends to remain under-discussed.

After eight months of working on my AI podcast platform, I’ve developed a straightforward strategy that I’ve dubbed the “3-Month Rule.” Essentially, any unscalable strategy is given a trial period of three months. At the end of this timeframe, it must demonstrate its worth through successful results—otherwise, it’s time to let it go.

As engineers, we are naturally inclined to create scalable solutions from the outset. Our training emphasizes robust architectures like microservices or distributed systems, tailored to handle vast user bases. Yet, in the early stages of a startup, focusing on scalability may not be the best use of our resources. My 3-Month Rule compels me to experiment with straightforward, less sophisticated coding solutions that prioritize shipping over long-term scalability, ultimately revealing the actual needs of my users.

My Current Strategies: Simplicity Over Complexity

1. Unified Infrastructure on a Single VM

Currently, my entire system—including the database, web server, background jobs, and caching—operates from a single $40-per-month virtual machine (VM). While this lack of redundancy may seem risky, it has provided invaluable insights into my resource requirements within just a couple of months. I learned that my platform, despite its AI intentions, only requires about 4GB of RAM at peak times. The elaborate Kubernetes architecture I had been considering would have been an unnecessary overhead.

When the system crashes (which it has a couple of times), it offers invaluable feedback regarding actual points of failure—information I’d never have anticipated.

2. Hardcoded Configurations

Configurations in my application are straightforward, involving constant definitions scattered through the code rather than relying on config files or environment variables:

python
PRICE_TIER_1 = 9.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While it may seem unrefined, this approach allows for rapid changes and easy tracking through Git. I’ve adjusted these values three times in three months—spending only a few minutes redeploying, as opposed to the many hours it would