The 3-Month Rule: A Strategic Approach to Building an AI Podcast Platform

In the world of technology startups, the mantra “Do things that don’t scale” has been famously echoed by entrepreneur Paul Graham. However, there is often a lack of discussion around how to effectively put this advice into practice during the coding phase. After eight months of developing my AI podcast platform, I have discovered a straightforward yet impactful framework: every unscalable solution is given a lifespan of three months. At the conclusion of this period, a choice must be made: either invest in refining it based on proven value or discontinue it altogether.

As engineers, we are conditioned to architect “scalable” solutions from the outset. We often find ourselves enamored with sophisticated designs—like microservices and distributed systems—that can accommodate millions of users. This, however, is a mindset more suited to larger conglomerates than to budding startups. In fact, early-stage scalable code may often serve as an expensive delay, focusing on future users and hypothetical problems instead of current realities. The three-month rule allows me to embrace a more straightforward approach, pushing me to write direct yet imperfect code that demonstrates real user needs.

Current Infrastructure Shortcuts: Insightful Choices

Here are some of the “unscalable hacks” I’ve implemented and the insights I’ve gained from them:

1. Consolidation on a Single Virtual Machine

I host my entire platform—including the database, web server, background processes, and caching—on a single $40-per-month virtual machine. While this setup lacks redundancy and requires manual backups, it has enabled me to quickly gauge my actual resource usage. I’ve discovered that my “AI-heavy” application peaks at just 4GB of RAM, revealing that the intricate Kubernetes setup I considered would have left me managing idle containers. Furthermore, when unexpected crashes occur, they provide genuine data about the vulnerabilities in my system, often revealing surprises about what fails.

2. Hardcoded Configurations for Simplicity

My app’s configurations are hardcoded directly into the code, such as:

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

Rather than utilizing config files or environment variables, this method might seem amateurish but offers significant benefits. I can easily search my entire codebase for any configuration via a simple grep command, and all