Embracing the Unscalable: A 3-Month Framework for Growth in Startups

In the startup world, the popular wisdom from Paul Graham rings true: “Do things that don’t scale.” But how does one effectively apply this principle in the realm of coding? After dedicating eight months to developing my AI podcast platform, I’ve found success with a streamlined approach: applying a three-month lifespan to every unscalable hack I implement. If it demonstrates its value in that time, it gets built out properly; if not, it gets scrapped.

As engineers, we’re often conditioned to pursue scalable solutions from the outset. We lean towards complex design patterns, microservices, and distributed systems, aiming to create architectures that can handle vast user populations. However, this mindset often doesn’t align with the reality faced by startups. In many cases, focusing on scalable code becomes a costly form of procrastination, as we start optimizing for users who may never materialize, rather than addressing the actual needs at hand. My three-month rule encourages me to create straightforward, albeit imperfect, code that can be deployed quickly, revealing the genuine requirements of my user base.

My Pragmatic Infrastructure Techniques and Their Clever Foundations

1. Consolidated Operations on a Single Virtual Machine

Currently, all critical components—database, web server, background jobs, and caching—are operating on a single virtual machine, costing just $40 a month. There’s no redundancy and backups are handled manually.

You may wonder how this could be a brilliant idea rather than a reckless one. This approach has allowed me to comprehend my actual resource requirements more effectively than any complex capacity planning report. I found that my AI-focused platform only peaks at 4GB of RAM. The sophisticated Kubernetes setup I almost pursued would have simply led me to manage a fleet of idle containers.

When failures occur—and they have, on two separate occasions—I gather invaluable insights about what truly causes disruptions. To my surprise, it’s never what I initially predicted.

2. Hardcoded Values in Place of Config Files

Imagine having constants like:

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

Instead of using configuration files or environment variables, these constants are directly integrated into my code. Modifying any values necessitates a redeployment.

What’s the upside? The simplicity allows me