Alexey Arustamov, director of an advanced analytics platform development company, conducted an experiment to test if AI could clone a complex corporate software product. The task was to replicate the platform based on public documentation, wikis, and demo examples within 48 hours, excluding source code and architectural schemes.
The resulting AI-generated system featured a modern web interface, ROC-curve visualization, and an anomaly detection module. However, deep functional testing revealed critical flaws. The system failed to integrate Python and JavaScript execution nodes into the data flow, and the OLAP cube ignored data volumes, displaying only 1,000 records while miscalculating totals.
Performance testing with 50 million rows caused browser crashes due to memory exhaustion, as the system lacked server-side lazy loading. Furthermore, the AI failed to implement actual parallel processing, defaulting to sequential execution despite claims of multi-threading. The code only functioned under ideal conditions and broke during edge-case testing.
The experiment highlighted that while AI excels at rapid prototyping and generating UI frameworks, it struggles with complex architectural dependencies and error handling. Relying on AI-generated code without rigorous manual review creates significant technical debt, as the model lacks the context of the entire system, leading to unmanageable codebases.
