Don’t Build to Scale on Your First Iteration (Plan for It Instead)

TL;DR: KISS Principle is still king, maybe even more so in the age of AI. The most difficult problems are people problems, and explaining what is important to business.

The Scaling Trap Every Software Team Falls Into

Every software project I’ve been involved in—from two-person startups to teams at national brands with seemingly unlimited resources—has fallen into the same trap: drastically underestimating the effort involved. Projects consistently go over budget and beyond timelines, regardless of who manages them or how detailed their Gantt charts are.

Why is this? We can land rovers on Mars with pinpoint accuracy, yet we struggle to predict the cost and timeline of software products. This isn’t just bad planning—it reveals fundamental misconceptions about software development.

Why Software Development Defies Prediction

Unlike other engineering disciplines, software development lacks standardized templates. Each software project tackles unique problems with unique data structures for use cases. Even platforms like Shopify require third-party assistance to fully meet user needs. When you have a strong vision, off-the-shelf solutions simply won’t cut it.

Here’s why custom software development remains so challenging:

1. Hyperevolution: Software development methodologies today are vastly different from just 12 months ago. Few engineering disciplines evolve this rapidly.
2. Vision-Execution Gap: Vision holders often lack understanding of software development complexities, while developers struggle to communicate these challenges effectively. This is where Product Management and Design become critical bridges.
3. Deceptive Progress Curve: What appears as rapid advancement in the first few months typically slows dramatically by months 6-10. The same effort yields diminishing returns.
4. No True “Final State”: The product journey doesn’t end with the last feature. User adoption initiates entirely new cycles of support, feedback, and development beyond the initial journey.

Photo by Wyron A on Unsplash

The Alternative: Progressive Scaling

Rather than building for scale from day one, successful teams I’ve worked with have adopted a different approach: planning for scale while building for current needs. This means:

• Building modular systems that can be replaced piece by piece
• Focusing on core functionality first, with clear pathways for expansion
• Creating flexible data models that won’t require painful migrations later
• Implementing monitoring from the beginning to identify bottlenecks early