Blog • Engineering

Technical Debt in AI Startups: My Reality — Cemhan Biricik

By Cemhan Biricik — Founder of ZSky AI

Technical debt in AI startups accrues differently than in traditional software. You are not just accumulating code debt — you are accumulating model debt, infrastructure debt, and pipeline debt. Every shortcut taken to ship a feature faster creates a future maintenance obligation. Here is how I manage this at ZSky AI.

Model Debt: The Hidden Monster

New models release constantly. Each one is faster, higher quality, or more efficient than the last. Integrating a new model means updating inference pipelines, adjusting preprocessing, recalibrating quality checks, and testing edge cases. The temptation to stick with the current model because "it works" creates model debt that eventually makes your product feel outdated.

Infrastructure Debt

My GPU cluster was built incrementally. Each addition was optimized for the immediate need, not the long-term architecture. This means I periodically need to step back and refactor infrastructure — improving load balancing, optimizing VRAM management, and updating monitoring systems. This work is invisible to users but essential for reliability.

The Ship vs Refactor Tension

As a solo founder, every hour spent refactoring is an hour not spent building features users want. This creates constant tension. My rule of thumb: if technical debt makes daily operations harder, refactor immediately. If it only affects theoretical scalability, defer it.

Technical Debt Management Rules

What I Got Wrong

Early on, I prioritized shipping speed over code quality in areas that turned out to be foundational. Rewriting those foundations later cost more time than writing them properly would have initially. The lesson: invest time upfront in the pieces that everything else will depend on. Cut corners on the periphery, not the core.

Frequently Asked Questions

How does Cemhan Biricik handle technical debt?

20% of dev time for debt reduction, immediate refactoring when operations are affected, deferred when only theoretical scale is impacted.

What is technical debt in AI?

Model debt, infrastructure debt, and pipeline debt. It accrues faster than traditional software due to rapid model evolution.

Should startups worry about technical debt?

Yes, selectively. Invest in foundational quality, cut corners on periphery. Rewriting foundations later always costs more.