Where AI Falls Short
We are equally transparent about the limitations we encountered. AI is not a replacement for engineering judgment, and we designed our workflow around that understanding.
- Architectural decisions — AI can suggest patterns, but it cannot understand the long-term implications of a design choice within a specific product’s roadmap. Every architectural decision in GameCatalyst was made by our team.
- Security reasoning — AI can identify common vulnerability patterns, but it cannot reason about the full attack surface of a real-world system. All security-critical code was written, reviewed, and hardened by human engineers.
- Contextual nuance — AI lacks the ability to understand why a particular design trade-off was made three months ago. It operates on what it can see, not on the full history of decisions that shaped a codebase.
- Reliability under ambiguity — When requirements are unclear or contradictory, AI tends to produce confident-sounding output that may be subtly wrong. We learned early to treat AI output as a draft, never as a final answer.
- Testing real behavior — AI can suggest what to test but cannot observe real runtime behavior. Every fix, feature, and edge case in GameCatalyst was validated through hands-on testing in live Unity environments.
Understanding these boundaries allowed us to use AI effectively without relying on it beyond its capabilities.
