Embracing Unscalable Solutions: The 3-Month Rule for Startups

In the ever-evolving landscape of tech entrepreneurship, there’s a well-known mantra from industry luminary Paul Graham: “Do things that don’t scale.” However, translating this wisdom into actionable strategies, particularly in software development, often goes unaddressed. After eight months of building my AI podcasting platform, I’ve devised a straightforward framework to apply this principle effectively: every unscalable solution is given a trial period of three months. If it proves its worth, it receives the investment needed for further development; otherwise, it gets phased out.

The startup Dilemma

As engineers, we frequently gravitate towards constructing scalable solutions right from the start. We’re trained to think in terms of complex infrastructures—microservices, distributed systems, and robust architectures designed to accommodate millions of users. Yet, this focus on scalability can often lead to costly delays for startups, diverting attention toward future concerns rather than immediate needs. The 3-month rule I follow compels me to prioritize writing straightforward, albeit imperfect, code that offers tangible results and insights about my users’ true requirements.

Current Unscalable Solutions and Their Value

1. Single-VM Operation

I host my database, web server, background jobs, and Redis on a single, budget-friendly $40/month virtual machine. This setup lacks redundancy and relies on manual backups to my local system. While it may seem reckless, the decision to consolidate resources has provided invaluable insights. In just two months, I’ve gained a clear understanding of my platform’s needs, discovering that it peaks at 4GB of RAM. The intricate Kubernetes architecture I nearly implemented would have merely dealt with idle resources. Each failure (there have been two) has offered real data on how systems fail—often in ways I didn’t anticipate.

2. Hardcoded Configuration

I manage configurations through constants sprinkled across my codebase, such as:

plaintext
PRICE_TIER_1 = 9.99
PRICE_TIER_2 = 19.99
MAX_USERS = 100
AI_MODEL = "gpt-4"

While this structure may seem primitive, it allows for rapid changes with a simple redeployment. I can swiftly search for any config value across my codebase, enabling efficient tracking and version control. Instead of spending a week building a configuration service, I’ve only had to modify these values three times