The 3-Month Experiment: A Strategy for Embracing Non-Scalable Solutions in Startups
In the startup world, Paul Graham’s advice to “do things that don’t scale” is widely acknowledged but rarely put into practice, especially in technical development. After immersing myself in building an AI podcast platform for the past eight months, I have devised a straightforward yet effective framework: every unscalable method has a three-month lifespan. During this window, it must either demonstrate its value and evolve into a solid solution or be discarded.
The natural inclination for engineers often leans toward creating scalable systems right from the beginning. Concepts like design patterns, microservices, and distributed architectures are the holy grail, enabling software to potentially manage millions of users. However, in a startup environment, focusing on scalability too early can lead to unnecessary complexities and delays, especially when you are merely delaying the inevitable.
Instead, my three-month rule compels me to develop straightforward, even “imperfect” code that can be deployed swiftly. This approach not only allows me to understand the actual needs of my users but also fosters genuine learning about the system itself.
Insightful Infrastructure Hacks Beneath the Surface
1. Consolidating Everything on One Virtual Machine
My entire stack—database, web server, background jobs, and caching—operates on a single $40/month virtual machine. This setup includes no redundancy and relies on manual backups.
You might wonder why this is a savvy approach. Simply put, I’ve gained unparalleled insights into my resource requirements in just two months. Initially labeled as “AI-heavy,” my platform only peaks at 4GB of RAM, thus confirming that my plan for a complex Kubernetes cluster would have been a waste of resources.
When crashes occur (yes, they’ve happened twice), I’m presented with real insights into what actually fails. Spoiler alert: it’s always something unexpected.
2. Relying on Hardcoded Configuration
Consider this snippet of code:
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"
There are no configuration files or environment variables; just constants scattered throughout the codebase. This method requires redeployment for any changes, yet it has its advantages.
The significant benefit? I can quickly search for any configuration value across my entire codebase. Each pricing update is meticulously recorded in
One Comment
This is a compelling approach that highlights the importance of embracing experimentation and lean development in the early stages. The “3-month rule” serves as a practical framework to balance agility with feedback—allowing startups to test ideas rapidly without over-investing in premature scalability.
Your insight about consolidating everything on a single VM resonates well; it emphasizes how simplicity can yield valuable understanding of actual resource needs and system weaknesses. Similarly, relying on hardcoded configurations accelerates iteration but underscores the importance of transitioning to more flexible setups as your product matures.
One point worth considering is how to evolve from these initial unscalable strategies toward sustainable, scalable infrastructure once your core assumptions are validated. Perhaps adopting a staged approach—where core components begin simple and gradually incorporate thoughtful scalability—can help manage technical debt while preserving agility.
Thanks for sharing this practical and thought-provoking methodology—it’s a valuable reminder that sometimes doing “things that don’t scale” early on can create the clarity and insights necessary for building robust, scalable solutions in the future.